{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:57Z","timestamp":1787027937216,"version":"build-2736575974"},"reference-count":266,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation (e.g., Osteosarcoma vs. Ewing sarcoma), and predicting mutation signatures (e.g., TP53, RB1). Moreover, we highlight the use of liquid biopsy and circulating tumor DNA (ctDNA) as emerging diagnostic biomarkers, coupled with point-of-care molecular assays, to enable early and accurate detection in low-resource settings. The review concludes by discussing translational barriers, including data harmonization, regulatory challenges, and the need for multi-institutional datasets to validate AI-based diagnostic frameworks. This article synthesizes current advancements and provides a forward-looking view of precision diagnostics in musculoskeletal oncology.<\/jats:p>","DOI":"10.3390\/diagnostics15111377","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T09:47:34Z","timestamp":1748512054000},"page":"1377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-2895","authenticated-orcid":false,"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5707-9009","authenticated-orcid":false,"given":"Kyle","family":"Sporn","sequence":"additional","affiliation":[{"name":"Norton College of Medicine, Upstate Medical University, Syracuse, NY 13210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4384-2693","authenticated-orcid":false,"given":"Akshay","family":"Khanna","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phani","family":"Paladugu","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA"},{"name":"Brigham and Women\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8177-2784","authenticated-orcid":false,"given":"Chirag","family":"Gowda","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Ngo","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Jagadeesan","sequence":"additional","affiliation":[{"name":"Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA"},{"name":"Cisco AI Systems, Cisco Inc., San Jose, CA 95134, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0120-0939","authenticated-orcid":false,"given":"Nasif","family":"Zaman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9460-1269","authenticated-orcid":false,"given":"Alireza","family":"Tavakkoli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1302\/0301-620X.92B11.24326","article-title":"Earlier Diagnosis of Bone and Soft-Tissue Tumours","volume":"92","author":"Grimer","year":"2010","journal-title":"J. Bone Jt. Surg. Br."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1007\/s00132-011-1821-7","article-title":"Diagnosis of Malignant Bone and Soft Tissue Tumors","volume":"40","author":"Rechl","year":"2011","journal-title":"Orthop\u00e4de"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.ejso.2004.11.016","article-title":"Delay in Referral to a Specialist Soft-Tissue Sarcoma Unit","volume":"31","author":"Clark","year":"2005","journal-title":"Eur. J. Surg. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shui, L., Ren, H., Yang, X., Li, J., Chen, Z., Yi, C., Zhu, H., and Shui, P. (2021). The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.570465"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3892\/ijo.2020.5063","article-title":"Artificial Intelligence Radiogenomics for Advancing Precision and Effectiveness in Oncologic Care (Review)","volume":"57","author":"Trivizakis","year":"2020","journal-title":"Int. J. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ferro, M., de Cobelli, O., Vartolomei, M.D., Lucarelli, G., Crocetto, F., Barone, B., Sciarra, A., Del Giudice, F., Muto, M., and Maggi, M. (2021). Prostate Cancer Radiogenomics\u2014From Imaging to Molecular Characterization. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22189971"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1148\/radiol.2016161382","article-title":"Radiogenomics of Glioblastoma: Machine Learning-Based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features","volume":"281","author":"Kickingereder","year":"2016","journal-title":"Radiology"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hinterwimmer, F., Guenther, M., Consalvo, S., Neumann, J., Gersing, A., W\u00f6rtler, K., von Eisenhart-Rothe, R., Burgkart, R., and Rueckert, D. (2024). Applications of Machine Learning for Imaging-Driven Diagnosis of Musculoskeletal Malignancies\u2014A Scoping Review. BMC Musculoskelet. Disord., 25.","DOI":"10.1186\/s12891-024-07934-9"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Breden, S., Hinterwimmer, F., Consalvo, S., Neumann, J., Knebel, C., von Eisenhart-Rothe, R., Burgkart, R.H., and Lenze, U. (2023). Deep Learning-Based Detection of Bone Tumors Around the Knee in X-Rays of Children. J. Clin. Med., 12.","DOI":"10.3390\/jcm12185960"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.canlet.2020.03.032","article-title":"Machine Learning in Oncology: A Clinical Appraisal","volume":"481","author":"Cuocolo","year":"2020","journal-title":"Cancer Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13244-020-00895-2","article-title":"Integrative Radiogenomics for Virtual Biopsy and Treatment Monitoring in Ovarian Cancer","volume":"11","author":"Rundo","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1089\/omi.2019.0142","article-title":"Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology","volume":"24","author":"Thomford","year":"2020","journal-title":"OMICS"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Anagnostopoulos, A.K., Gaitanis, A., Gkiozos, I., Athanasiadis, E.I., Chatziioannou, S.N., Syrigos, K.N., Thanos, D., Chatziioannou, A.N., and Papanikolaou, N. (2022). Radiomics\/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results. Cancers, 14.","DOI":"10.3390\/cancers14071657"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1159\/000493575","article-title":"Machine Learning and Imaging Informatics in Oncology","volume":"98","author":"Tseng","year":"2020","journal-title":"Oncology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Saxena, S., Jena, B., Gupta, N., Das, S., Sarmah, D., Bhattacharya, P., Nath, T., Paul, S., Fouda, M.M., and Kalra, M. (2022). Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers, 14.","DOI":"10.3390\/cancers14122860"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1148\/radiol.2018181928","article-title":"Emerging Applications of Artificial Intelligence in Neuro-Oncology","volume":"290","author":"Rudie","year":"2019","journal-title":"Radiology"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, B.T., Chen, Z., Ye, N., Mambetsariev, I., Fricke, J., Daniel, E., Wang, G., Wong, C.W., Rockne, R.C., and Colen, R.R. (2020). Differentiating Peripherally-Located Small Cell Lung Cancer from Non-Small Cell Lung Cancer Using a CT Radiomic Approach. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.00593"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mun, S.K., Wong, K.H., Lo, S.B., Li, Y., and Bayarsaikhan, S. (2020). Artificial Intelligence for the Future Radiology Diagnostic Service. Front. Mol. Biosci., 7.","DOI":"10.3389\/fmolb.2020.614258"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1007\/s11307-020-01487-8","article-title":"Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms","volume":"22","author":"Shiri","year":"2020","journal-title":"Mol. Imaging Biol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tu, S.J., Wang, C.W., Pan, K.T., Wu, Y.C., and Wu, C.T. (2018). Localized Thin-Section CT with Radiomics Feature Extraction and Machine Learning to Classify Early-Detected Pulmonary Nodules from Lung Cancer Screening. Phys. Med. Biol., 63.","DOI":"10.1088\/1361-6560\/aaafab"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2018.02.015","article-title":"Radiomics-Based Features for Pattern Recognition of Lung Cancer Histopathology and Metastases","volume":"159","author":"Cipriano","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","unstructured":"Bourbonne, V., Vallieres, M., Lucia, F., Doucet, L., Visvikis, D., Tixier, F., Le Prise, E., Hatt, M., and Schick, U. (2022). MRI-Derived Radiomics to Guide Post-Operative Management for High-Risk Prostate Cancer Patients. Cancers, 14."},{"key":"ref_24","first-page":"20","article-title":"External Validation of a Radiomic Signature to Predict p16 Status and Outcome in Oropharyngeal Cancers","volume":"152","author":"Bourbonne","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Grochans, S., Cybulska, A.M., Simi\u0144ska, D., Korbecki, J., Kojder, K., Chlubek, D., and Baranowska-Bosiacka, I. (2022). Epidemiology of Glioblastoma Multiforme\u2014Literature Review. Cancers, 14.","DOI":"10.3390\/cancers14102412"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fanizzi, A., Catino, A., Bove, S., Comes, M.C., Montrone, M., Sicolo, A., Signorile, R., Perrotti, P., Pizzutilo, P., and Galetta, D. (2024). Transfer Learning Approach in Pre-Treatment CT Images to Predict Therapeutic Response in Advanced Malignant Pleural Mesothelioma. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1432188"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.1038\/s41467-021-23995-z","article-title":"Tumor-Associated Hematopoietic Stem and Progenitor Cells Positively Linked to Glioblastoma Progression","volume":"12","author":"Lu","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kim, H.J., Park, J.W., and Lee, J.H. (2021). Genetic Architectures and Cell-of-Origin in Glioblastoma. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.615400"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1007\/s13311-022-01251-6","article-title":"Updates in IDH-Wildtype Glioblastoma","volume":"19","author":"Melhem","year":"2022","journal-title":"Neurotherapeutics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jocn.2018.05.002","article-title":"Ten-Year Survival in Glioblastoma. A Systematic Review","volume":"54","author":"Tykocki","year":"2018","journal-title":"J. Clin. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jena, B., Saxena, S., Nayak, G.K., Balestrieri, A., Gupta, N., Khanna, N.N., Laird, J.R., Kalra, M.K., Fouda, M.M., and Saba, L. (2022). Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework. Cancers, 14.","DOI":"10.3390\/cancers14164052"},{"key":"ref_32","unstructured":"Wikimedia Commons (2025, May 20). File:Cancer Biomarker Figure.png. Published 30 January 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:Cancer_biomarker_figure.png&oldid=847271038."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2478\/raon-2020-0068","article-title":"Artificial Intelligence in Musculoskeletal Oncological Radiology","volume":"55","author":"Vogrin","year":"2020","journal-title":"Radiol. Oncol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.compmedimag.2017.05.002","article-title":"Transfer Learning on Fused Multiparametric MR Images for Classifying Histopathological Subtypes of Rhabdomyosarcoma","volume":"65","author":"Banerjee","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_35","unstructured":"Haubold, J., Hosch, R., Parmar, V., Glas, M., Guberina, N., Catalano, O.A., Pierscianek, D., Wrede, K., Deuschl, C., and Forsting, M. (2023). Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas. Cancers, 15."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ma, F., Shao, X., Zhang, Y., Li, J., Li, Q., Sun, H., Wang, T., Liu, H., Zhao, F., and Chen, L. (2024). An Arterial Spin Labeling-Based Radiomics Signature and Machine Learning for the Prediction and Detection of Various Stages of Kidney Damage Due to Diabetes. Front. Endocrinol., 15.","DOI":"10.3389\/fendo.2024.1333881"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Feng, Z., Kong, D., Jin, W., He, K., Zhao, J., Liu, B., Xu, H., Yu, X., and Feng, S. (2023). Rapid Detection of Isocitrate Dehydrogenase 1 Mutation Status in Glioma Based on Crispr-Cas12a. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-32957-y"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1200\/CCI.20.00049","article-title":"Machine Learning in Oncology: What Should Clinicians Know?","volume":"4","author":"Nagy","year":"2020","journal-title":"JCO Clin. Cancer Inform."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1002\/jcu.23321","article-title":"Artificial Intelligence, Machine Learning and Deep Learning in Musculoskeletal Imaging: Current Applications","volume":"50","author":"Caudo","year":"2022","journal-title":"J. Clin. Ultrasound"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1007\/s11547-021-01389-x","article-title":"A Deep Look into Radiomics","volume":"126","author":"Scapicchio","year":"2021","journal-title":"Radiol. Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pitarch, C., Ungan, G., Juli\u00e0-Sap\u00e9, M., and Vellido, A. (2024). Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers, 16.","DOI":"10.3390\/cancers16020300"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1097\/WCO.0000000000000761","article-title":"Applications of Artificial Intelligence in Neuro-Oncology","volume":"32","author":"Aneja","year":"2019","journal-title":"Curr. Opin. Neurol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1111\/cas.14377","article-title":"Artificial Intelligence in Oncology","volume":"111","author":"Shimizu","year":"2020","journal-title":"Cancer Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kawaguchi, R.K., Takahashi, M., Miyake, M., Kinoshita, M., Takahashi, S., Ichimura, K., Hamamoto, R., Narita, Y., and Sese, J. (2021). Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals. Cancers, 13.","DOI":"10.3390\/cancers13143611"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2130","DOI":"10.1007\/s10439-023-03304-z","article-title":"Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence","volume":"51","author":"Paladugu","year":"2023","journal-title":"Ann. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5510","DOI":"10.1007\/s00330-020-06874-x","article-title":"Implementation of eHealth and AI Integrated Diagnostics with Multidisciplinary Digitized Data: Are We Ready from an International Perspective?","volume":"30","author":"Bukowski","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chang, Y., Park, H., Yang, H.J., Lee, S., Lee, K.Y., Kim, T.S., Shin, J., and Nam, D.H. (2018). Cancer Drug Response Profile Scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-27214-6"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1038\/ng.2762","article-title":"Emerging Landscape of Oncogenic Signatures across Human Cancers","volume":"45","author":"Ciriello","year":"2013","journal-title":"Nat. Genet."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1177\/117693510600200030","article-title":"Applications of Machine Learning in Cancer Prediction and Prognosis","volume":"2","author":"Cruz","year":"2006","journal-title":"Cancer Inform."},{"key":"ref_50","unstructured":"(2025, May 20). \u201cFile:Flowchart of the Standard Radiomics Model.png.\u201d Wikimedia Commons. 9 October 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:Flowchart_of_the_standard_radiomics_model.png&oldid=935994688."},{"key":"ref_51","unstructured":"(2025, May 20). Cancer Pharmacogenomics.png. Wikimedia Commons. Published 30 January 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:Cancer_pharmacogenomics.png&oldid=847271761."},{"key":"ref_52","unstructured":"(2025, May 20). The First and Second Phases of the NIH Human Microbiome Project.png. Wikimedia Commons. Published 8 November 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:The_first_and_second_phases_of_the_NIH_Human_Microbiome_Project.png&oldid=954301671."},{"key":"ref_53","unstructured":"(2025, May 20). MultiLayerNeuralNetworkBigger english.png. Wikimedia Commons. Published 15 July 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:MultiLayerNeuralNetworkBigger_english.png&oldid=898886601."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1038\/s41416-023-02317-8","article-title":"Radiogenomics: A key component of precision cancer medicine","volume":"129","author":"Liu","year":"2023","journal-title":"Br. J. Cancer"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1186\/s40644-024-00769-6","article-title":"New Frontiers in Domain-Inspired Radiomics and Radiogenomics: Increasing Role of Molecular Diagnostics in CNS Tumor Classification and Grading Following WHO CNS-5 Updates","volume":"24","author":"Singh","year":"2024","journal-title":"Cancer Imaging"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, J., Hao, L., Qi, M., Xu, Q., Zhang, N., Feng, H., and Shi, G. (2023). Radiomics Nomogram for Preoperative Differentiation of Pulmonary Mucinous Adenocarcinoma from Tuberculoma in Solitary Pulmonary Solid Nodules. BMC Cancer, 23.","DOI":"10.1186\/s12885-023-10734-4"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"597","DOI":"10.2147\/OTT.S352619","article-title":"EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma","volume":"15","author":"Chen","year":"2022","journal-title":"OncoTargets Ther."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1186\/s13244-024-01831-4","article-title":"The Prognostic Value of Radiogenomics Using CT in Patients with Lung Cancer: A Systematic Review","volume":"15","author":"Jiang","year":"2024","journal-title":"Insights Imaging"},{"key":"ref_59","first-page":"100586","article-title":"A Radiomics Model for Predicting the Response to Bevacizumab in Brain Necrosis after Nasopharyngeal Carcinoma Radiotherapy","volume":"39","author":"Miles","year":"2023","journal-title":"Clin. Transl. Radiat. Oncol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.adro.2019.02.003","article-title":"MRI Radiomic Features Are Independently Associated with Overall Survival in Soft Tissue Sarcoma","volume":"4","author":"Spraker","year":"2019","journal-title":"Adv. Radiat. Oncol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.ebiom.2018.07.006","article-title":"Survival Prediction in High-Grade Osteosarcoma Using Radiomics of Diagnostic Computed Tomography","volume":"34","author":"Wu","year":"2018","journal-title":"EBioMedicine"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","article-title":"Big Data and Machine Learning Algorithms for Health-Care Delivery","volume":"20","author":"Ngiam","year":"2019","journal-title":"Lancet Oncol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lu, S.C., Swisher, C.L., Chung, C., Jaffray, D., and Sidey-Gibbons, C. (2023). On the Importance of Interpretable Machine Learning Predictions to Inform Clinical Decision Making in Oncology. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1129380"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2708","DOI":"10.1016\/j.csbj.2024.06.019","article-title":"Deep Learning of Radiology-Genomics Integration for Computational Oncology: A Mini Review","volume":"23","author":"Wang","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Shams, A. (2024). Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment. Diagnostics, 14.","DOI":"10.3390\/diagnostics14192174"},{"key":"ref_66","first-page":"e69818","article-title":"The Role of Artificial Intelligence in the Diagnosis of Melanoma","volume":"16","author":"Kalidindi","year":"2024","journal-title":"Cureus"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1007\/s11684-019-0726-4","article-title":"Survey on Deep Learning for Pulmonary Medical Imaging","volume":"14","author":"Ma","year":"2020","journal-title":"Front. Med."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Paranavithana, I.R., Stirling, D., Ros, M., and Field, M. (2023). Systematic Review of Tumor Segmentation Strategies for Bone Metastases. Cancers, 15.","DOI":"10.3390\/cancers15061750"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1055\/s-0039-3400268","article-title":"Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics","volume":"24","author":"Favre","year":"2020","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Debs, P., and Fayad, L.M. (2023). The Promise and Limitations of Artificial Intelligence in Musculoskeletal Imaging. Front. Radiol., 3.","DOI":"10.3389\/fradi.2023.1242902"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Montin, E., Kijowski, R., Youm, T., and Lattanzi, R. (2023). A Radiomics Approach to the Diagnosis of Femoroacetabular Impingement. Front. Radiol., 3.","DOI":"10.3389\/fradi.2023.1151258"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gohla, G., Kraus, M.S., Peyker, I., Springer, F., and Keller, G. (2023). Diagnostic Accuracy of 128-Slice Single-Source CT for the Detection of Dislocated Bucket Handle Meniscal Tears in the Setting of an Acute Knee Trauma\u2014Correlation with MRI and Arthroscopy. Diagnostics, 13.","DOI":"10.3390\/diagnostics13071295"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s00256-021-03862-0","article-title":"AI MSK Clinical Applications: Spine Imaging","volume":"51","author":"Huber","year":"2022","journal-title":"Skeletal Radiol."},{"key":"ref_74","first-page":"3","article-title":"Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift","volume":"24","author":"Razavian","year":"2020","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Smith, S.J., Moorin, R., Taylor, K., Newton, J., and Smith, S. (2024). Collecting Routine and Timely Cancer Stage at Diagnosis by Implementing a Cancer Staging Tiered Framework: The Western Australian Cancer Registry Experience. BMC Health Serv. Res., 24.","DOI":"10.1186\/s12913-024-11224-4"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1055\/a-1909-7013","article-title":"Artificial Intelligence in Oncological Hybrid Imaging","volume":"195","author":"Feuerecker","year":"2023","journal-title":"Rofo"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/s11912-021-01054-6","article-title":"Role of Machine Learning and Artificial Intelligence in Interventional Oncology","volume":"23","author":"Daye","year":"2021","journal-title":"Curr. Oncol. Rep."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"100368","DOI":"10.1016\/j.jpi.2024.100368","article-title":"Utility of artificial intelligence in a binary classification of soft tissue tumors","volume":"15","author":"Di","year":"2024","journal-title":"J. Pathol Inform."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1007\/s10278-023-00945-3","article-title":"Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: A Meta-Analysis","volume":"37","author":"Salehi","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ong, W., Zhu, L., Tan, Y.L., Teo, E.C., Tan, J.H., Kumar, N., Vellayappan, B.A., Ooi, B.C., Quek, S.T., and Makmur, A. (2023). Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review. Cancers, 15.","DOI":"10.3390\/cancers15061837"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Choi, J.H., and Ro, J.Y. (2023). The Recent Advances in Molecular Diagnosis of Soft Tissue Tumors. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24065934"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3322\/caac.21552","article-title":"Artificial intelligence in cancer imaging: Clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1097\/00003086-198903000-00036","article-title":"Diagnostic difficulties in skeletal pathology","volume":"240","author":"Schajowicz","year":"1989","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to Radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J. Nucl. Med."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1148\/rg.2021210037","article-title":"Radiomics in Oncology: A Practical Guide","volume":"41","author":"Shur","year":"2021","journal-title":"Radiographics"},{"key":"ref_89","first-page":"635","article-title":"Radiomics with Artificial Intelligence: A Review for the Nonradiologist","volume":"27","author":"Kocak","year":"2021","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_90","first-page":"795","article-title":"Artificial Intelligence in Breast Cancer Imaging: Risk Stratification, Lesion Detection and Classification, Treatment Planning and Prognosis\u2014A Narrative Review","volume":"3","author":"Caloro","year":"2022","journal-title":"Explor. Target. Antitumor Ther."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.7150\/thno.30309","article-title":"The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges","volume":"9","author":"Liu","year":"2019","journal-title":"Theranostics"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s00330-019-06360-z","article-title":"Quality of Science and Reporting of Radiomics in Oncologic Studies: Room for Improvement According to Radiomics Quality Score and TRIPOD Statement","volume":"30","author":"Park","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"42380","DOI":"10.18632\/oncotarget.5675","article-title":"Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation","volume":"6","author":"Jiang","year":"2015","journal-title":"Oncotarget"},{"key":"ref_94","unstructured":"Radiomic Features\u2014Pyradiomics 2.2.0.post35+g8da1db7 Documentation (2025, May 05). Readthedocs.io. Published 2016., Available online: https:\/\/pyradiomics.readthedocs.io\/en\/latest\/features.html."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"3922","DOI":"10.1158\/0008-5472.CAN-17-0122","article-title":"Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer","volume":"77","author":"Parmar","year":"2017","journal-title":"Cancer Res."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Fusco, R., Sansone, M., Granata, V., Di Bonito, M., Avino, F., Catalano, O., Botti, G., and Petrillo, A. (2018). Use of Quantitative Morphological and Functional Features for Assessment of Axillary Lymph Node in Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Biomed. Res. Int., 2018.","DOI":"10.1155\/2018\/2610801"},{"key":"ref_97","unstructured":"Zubair, A.R., and Alo, O.A. (2025, May 15). Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis. arXiv, Available online: https:\/\/arxiv.org\/abs\/2403.04038."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"R150","DOI":"10.1088\/0031-9155\/61\/13\/R150","article-title":"Applications and Limitations of Radiomics","volume":"61","author":"Yip","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1093\/annonc\/mdx034","article-title":"Promises and Challenges for the Implementation of Computational Medical Imaging (Radiomics) in Oncology","volume":"28","author":"Limkin","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s41747-018-0068-z","article-title":"Radiomics: The Facts and the Challenges of Image Analysis","volume":"2","author":"Rizzo","year":"2018","journal-title":"Eur. Radiol. Exp."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13058-019-1187-z","article-title":"Radiomic Signatures with Contrast-Enhanced Magnetic Resonance Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes: Initial Results","volume":"21","author":"Leithner","year":"2019","journal-title":"Breast Cancer Res."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1007\/s00261-019-02028-w","article-title":"Radiogenomics: Bridging Imaging and Genomics","volume":"44","author":"WBodalal","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_104","first-page":"472","article-title":"Utility of Quantitative Magnetic Resonance Imaging Parameters in the Evaluation of Soft Tissue Sarcomas","volume":"49","author":"Winfield","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1002\/jmri.26589","article-title":"T2-Based MRI Delta-Radiomics Improve Response Prediction in Soft-Tissue Sarcomas Treated by Neoadjuvant Chemotherapy","volume":"50","author":"Kind","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"5471","DOI":"10.1088\/0031-9155\/60\/14\/5471","article-title":"A Radiomics Model from Joint FDG-PET and MRI Texture Features for the Prediction of Lung Metastases in Soft-Tissue Sarcomas of the Extremities","volume":"60","author":"Freeman","year":"2015","journal-title":"Phys. Med. Biol."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"66","DOI":"10.18383\/j.tom.2018.00009","article-title":"Dual-Energy CT-Derived Iodine Content and Spectral Attenuation Analysis of Metastatic Versus Nonmetastatic Lymph Nodes in Squamous Cell Carcinoma of the Oropharynx","volume":"4","author":"Nakajo","year":"2018","journal-title":"Tomography."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Gaillard, F. (2025, May 20). Hounsfield Unit Formula. Case Study, Radiopaedia.org. Available online: https:\/\/doi.org\/10.53347\/rID-147911.","DOI":"10.53347\/rID-147911"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.ejmp.2018.03.012","article-title":"Radiomics in Radiooncology\u2014Challenging the Medical Physicist","volume":"48","author":"Peeken","year":"2018","journal-title":"Phys. Med."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Long, H., Zhang, P., Bi, Y., Yang, C., Wu, M., He, D., Huang, S., Yang, K., Qi, S., and Wang, J. (2023). MRI Radiomic Features of Peritumoral Edema May Predict the Recurrence Sites of Glioblastoma Multiforme. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.1042498"},{"key":"ref_111","first-page":"947","article-title":"Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma","volume":"47","author":"Gao","year":"2023","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Kumar, R., Sporn, K., Ong, J., Waisberg, E., Paladugu, P., Vaja, S., Hage, T., Sekhar, T.C., Vadhera, A.S., and Ngo, A. (2025). Integrating Artificial Intelligence in Orthopedic Care: Advancements in Bone Care and Future Directions. Bioengineering, 12.","DOI":"10.20944\/preprints202504.0576.v1"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s12672-024-00908-2","article-title":"The Impact of Radiomics in the Management of Soft Tissue Sarcoma","volume":"15","author":"Casale","year":"2024","journal-title":"Discov. Oncol."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Bailly, C., Bodet-Milin, C., Couespel, S., Necib, H., Kraeber-Bod\u00e9r\u00e9, F., Ansquer, C., and Carlier, T. (2016). Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0159984"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s00259-017-3855-5","article-title":"FDG PET\/CT Radiomics for Predicting the Outcome of Locally Advanced Rectal Cancer","volume":"45","author":"Lovinfosse","year":"2018","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S1095-0397(99)00016-3","article-title":"Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis","volume":"2","author":"Larson","year":"1999","journal-title":"Clin. Positron Imaging"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s13139-017-0493-6","article-title":"Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better?","volume":"52","author":"Im","year":"2018","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s13139-019-00571-4","article-title":"Radiomics in Oncological PET\/CT: A Methodological Overview","volume":"53","author":"Ha","year":"2019","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.3892\/ol.2016.4765","article-title":"Impact of total lesion glycolysis measured by 18F-FDG-PET\/CT on overall survival and distant metastasis in hypopharyngeal cancer","volume":"12","author":"Suzuki","year":"2016","journal-title":"Oncol Lett."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.2967\/jnumed.117.199935","article-title":"A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET","volume":"59","author":"Orlhac","year":"2018","journal-title":"J. Nucl. Med."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1016\/j.ijrobp.2016.03.038","article-title":"In Regard to Mattonen et al","volume":"95","author":"Sun","year":"2016","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1016\/j.ijrobp.2018.05.053","article-title":"Repeatability and Reproducibility of Radiomic Features: A Systematic Review","volume":"102","author":"Traverso","year":"2018","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","article-title":"Radiomics: The Process and the Challenges","volume":"30","author":"Kumar","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_124","unstructured":"File:TORNAI-SpectrumOfMedicalImaging.jpg (2025, May 20). Wikimedia Commons. Published 5 November 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:TORNAI-SpectrumOfMedicalImaging.jpg&oldid=953058884."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Parmar, C., Grossmann, P., Bussink, J., Lambin, P., and Aerts, H.J.W.L. (2015). Machine Learning Methods for Quantitative Radiomic Biomarkers. Sci. Rep., 5.","DOI":"10.3389\/fonc.2015.00272"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Leger, S., Zwanenburg, A., Pilz, K., Lohaus, F., Linge, A., Z\u00f6phel, K., Kotzerke, J., Schreiber, A., Tissot, V., and Dreveau, C. (2017). A Comparative Study of Machine Learning Methods for Time-to-Event Survival Data for Radiomics Risk Modelling. Sci. Rep., 7.","DOI":"10.1038\/s41598-017-13448-3"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1007\/s00330-021-08146-8","article-title":"Radiomic Machine Learning for Predicting Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Using Tumor Heterogeneity and Angiogenesis Properties on MRI","volume":"32","author":"Lee","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","article-title":"Synthetic Data in Machine Learning for Medicine and Healthcare","volume":"5","author":"Chen","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"HajiEsmailPoor, Z., Tabnak, P., Baradaran, B., Pashazadeh, F., and Aghebati-Maleki, L. (2023). Diagnostic Performance of CT Scan-Based Radiomics for Prediction of Lymph Node Metastasis in Gastric Cancer: A Systematic Review and Meta-Analysis. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1185663"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Stanzione, A., Cuocolo, R., Ugga, L., Verde, F., Romeo, V., Mainenti, P.P., Brunetti, A., and Maurea, S. (2022). Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030578"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-Based Phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1148\/radiol.2019182023","article-title":"Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics","volume":"291","author":"Orlhac","year":"2019","journal-title":"Radiology"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Da-Ano, R., Visvikis, D., and Hatt, M. (2020). Harmonization Strategies for Multicenter Radiomics Investigations. Phys. Med. Biol., 65.","DOI":"10.1088\/1361-6560\/aba798"},{"key":"ref_134","first-page":"1","article-title":"Texture Analysis of Medical Images in Radiomics: Review","volume":"79","author":"Scalco","year":"2020","journal-title":"Phys. Med."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1148\/radiol.2018172361","article-title":"Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters","volume":"288","author":"Berenguer","year":"2018","journal-title":"Radiology"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Schwier, M., van Griethuysen, J., Vangel, M.G., Pieper, S., Peled, S., Tempany, C., Aerts, H.J.W.L., Kikinis, R., Fennessy, F.M., and Fedorov, A. (2019). Repeatability of Multiparametric Prostate MRI Radiomics Features. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-45766-z"},{"key":"ref_137","first-page":"e50","article-title":"Temporal Changes of Quantitative CT Findings from 102 Patients with COVID-19 in Wuhan, China: A Longitudinal Study","volume":"36","author":"Park","year":"2021","journal-title":"J. Korean Med. Sci."},{"key":"ref_138","unstructured":"(2025, May 20). \u201cFile:K-fold Cross Validation EN.svg.\u201d Wikimedia Commons. 3 October 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:K-fold_cross_validation_EN.svg&oldid=932198002."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in Medical Imaging\u2014\u201cHow-To\u201d Guide and Critical Reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"569","DOI":"10.37349\/etat.2023.00153","article-title":"Emerging Role of Quantitative Imaging (Radiomics) and Artificial Intelligence in Precision Oncology","volume":"4","author":"Jha","year":"2023","journal-title":"Explor. Target. Antitumor Ther."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ymeth.2020.05.022","article-title":"Radiomics for Precision Medicine: Current Challenges, Future Prospects, and the Proposed 6Rs Framework","volume":"188","author":"Ibrahim","year":"2021","journal-title":"Methods"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Zhao, B., Tan, Y., Tsai, W.Y., Qi, J., Xie, C., Lu, L., and Schwartz, L.H. (2016). Reproducibility of Radiomics for Deciphering Tumor Phenotype with Imaging. Sci. Rep., 6.","DOI":"10.1038\/srep23428"},{"key":"ref_143","first-page":"20190026","article-title":"Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Modeling","volume":"1","author":"Altazi","year":"2019","journal-title":"BJR Open"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"660","DOI":"10.18632\/oncotarget.26578","article-title":"Preoperative MRI-Radiomics Features Improve Prediction of Survival in Glioblastoma Patients over MGMT Methylation Status Alone","volume":"10","author":"Tixier","year":"2019","journal-title":"Oncotarget"},{"key":"ref_145","first-page":"173","article-title":"Radiomics as a Personalized Medicine Tool in Lung Cancer: Predicting Treatment Response from Baseline Imaging","volume":"23","author":"Price","year":"2022","journal-title":"Clin. Lung Cancer"},{"key":"ref_146","unstructured":"(2025, May 20). \u201cFile:Real-to-Voxel-Intensity-Range.png.\u201d Wikimedia Commons. 12 May 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:Real-to-Voxel-intensity-range.png&oldid=876059423."},{"key":"ref_147","unstructured":"Zwanenburg, A., Leger, S., Valli\u00e8res, M., and L\u00f6ck, S. (2016). For the Image Biomarker Standardization Initiative. Image Biomarker Standardisation Initiative. arXiv."},{"key":"ref_148","first-page":"380","article-title":"The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview","volume":"11993","author":"Pati","year":"2020","journal-title":"Brainlesion"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Chalkidou, A., O\u2019Doherty, M.J., and Marsden, P.K. (2015). False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0124165"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s41512-020-00074-3","article-title":"State of the Art in Selection of Variables and Functional Forms in Multivariable Analysis\u2014Outstanding Issues","volume":"4","author":"Sauerbrei","year":"2020","journal-title":"Diagn. Progn. Res."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.radonc.2018.10.027","article-title":"Vulnerabilities of Radiomic Signature Development: The need for safeguards","volume":"130","author":"Welch","year":"2019","journal-title":"Radiother Oncol."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1016\/j.cell.2017.10.014","article-title":"Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas","volume":"171","author":"Abeshouse","year":"2017","journal-title":"Cell"},{"key":"ref_153","first-page":"385","article-title":"Targeted Therapies for Sarcomas: New Frontiers in Precision Medicine","volume":"23","author":"Antonescu","year":"2021","journal-title":"J. Mol. Diagn."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1158\/2159-8290.CD-17-1152","article-title":"Genome-Informed Targeted Therapy for Osteosarcoma","volume":"9","author":"Sayles","year":"2019","journal-title":"Cancer Discov."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1038\/nm.4273","article-title":"DNA Methylation Heterogeneity Defines a Disease Spectrum in Ewing Sarcoma","volume":"23","author":"Sheffield","year":"2017","journal-title":"Nat. Med."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.celrep.2014.03.003","article-title":"Recurrent Somatic Structural Variations Contribute to Tumorigenesis in Pediatric Osteosarcoma","volume":"7","author":"Chen","year":"2014","journal-title":"Cell Rep."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1093\/hmg\/ddp117","article-title":"Identification of Interactive Networks of Gene Expression Associated with Osteosarcoma Oncogenesis by Integrated Molecular Profiling","volume":"18","author":"Sadikovic","year":"2009","journal-title":"Hum. Mol. Genet."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Wu, C.C., Beird, H.C., Andrew Livingston, J., Advani, S., Akdemir, K.C., Mitra, A., Kim, S., Ingram, D.R., Wang, W.L., and Lazar, A.J. (2020). Immuno-Genomic Landscape of Osteosarcoma. Front. Immunol., 11.","DOI":"10.1038\/s41467-020-14646-w"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.ccell.2014.10.004","article-title":"EWS-FLI1 Utilizes Divergent Chromatin Remodeling Mechanisms to Directly Activate or Repress Enhancer Elements in Ewing Sarcoma","volume":"26","author":"Riggi","year":"2014","journal-title":"Cancer Cell"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/s41586-020-2093-3","article-title":"An Atlas of Dynamic Chromatin Landscapes in Mouse Fetal Development","volume":"583","author":"Gorkin","year":"2020","journal-title":"Nature"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Orrapin, S., Moonmuang, S., Udomruk, S., Yongpitakwattana, P., Pruksakorn, D., and Chaiyawat, P. (2024). Unlocking the Tumor-Immune Microenvironment in Osteosarcoma: Insights into the Immune Landscape and Mechanisms. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1394284"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Tatsuno, R., Komohara, Y., Pan, C., Kawasaki, T., Enomoto, A., Jubashi, T., Kono, H., Wako, M., Ashizawa, T., and Haro, H. (2024). Surface Markers and Chemokines\/Cytokines of Tumor-Associated Macrophages in Osteosarcoma and Other Carcinoma Microenvironments\u2014Contradictions and Comparisons. Cancers, 16.","DOI":"10.3390\/cancers16162801"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Gong, L., Sun, X., and Jia, M. (2023). New Gene Signature from the Dominant Infiltration Immune Cell Type in Osteosarcoma Predicts Overall Survival. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-45566-6"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Zhou, J., Zhao, L., Xiao, Y., Xie, S., Long, Y., Wei, Y., Meng, Q., Li, X., Luo, H., and Zhu, H. (2022). The Expression of Cytokine Profiles and Related Receptors in Idiopathic Inflammatory Myopathies. Front. Pharmacol., 13.","DOI":"10.3389\/fphar.2022.852055"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Kany, S., Vollrath, J.T., and Relja, B. (2019). Cytokines in Inflammatory Disease. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20236008"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Kartikasari, A.E.R., Huertas, C.S., Mitchell, A., and Plebanski, M. (2021). Tumor-Induced Inflammatory Cytokines and the Emerging Diagnostic Devices for Cancer Detection and Prognosis. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.692142"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1002\/cncr.25538","article-title":"Trends in Survival for Patients with Metastatic Soft-Tissue Sarcoma","volume":"117","author":"Italiano","year":"2011","journal-title":"Cancer"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"76241","DOI":"10.18632\/oncotarget.19532","article-title":"Integration of genomic, transcriptomic and functional profiles of aggressive osteosarcomas across multiple species","volume":"8","author":"Davis","year":"2017","journal-title":"Oncotarget"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Frazzette, N., and Jour, G. (2025). Novel Molecular Methods in Soft Tissue Sarcomas: From Diagnostics to Theragnostics. Cancers, 17.","DOI":"10.3390\/cancers17071215"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Zou, Z., Sun, W., Xu, Y., Liu, W., Zhong, J., Lin, X., and Chen, Y. (2022). Application of Multi-Omics Approach in Sarcomas: A Tool for Studying Mechanism, Biomarkers, and Therapeutic Targets. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.946022"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"6848","DOI":"10.1182\/blood-2024-205264","article-title":"Development of a Spatial Multiomics Platform to Integrate Genomic, Transcriptomic and Proteomic Features for Translational Research in Multiple Myeloma","volume":"144","author":"Gooding","year":"2024","journal-title":"Blood"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"e722","DOI":"10.1002\/mco2.722","article-title":"Radiogenomics: Bridging the gap between imaging and genomics for precision oncology","volume":"5","author":"He","year":"2024","journal-title":"MedComm. (2020)"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.jhep.2024.10.043","article-title":"Circulating tumor DNA status and dynamics predict recurrence in patients with resected extrahepatic cholangiocarcinoma","volume":"82","author":"Yoo","year":"2025","journal-title":"J. Hepatol."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1242\/dmm.018036","article-title":"Genetically Modified T Cells in Cancer Therapy: Opportunities and Challenges","volume":"8","author":"Sharpe","year":"2015","journal-title":"Dis. Model. Mech."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"eadf0837","DOI":"10.1126\/sciadv.adf0837","article-title":"Radiogenomic-Based Multiomic Analysis Reveals Imaging Intratumor Heterogeneity Phenotypes and Therapeutic Targets","volume":"9","author":"Su","year":"2023","journal-title":"Sci. Adv."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1148\/radiol.2017161845","article-title":"Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications","volume":"286","author":"Zhou","year":"2018","journal-title":"Radiology"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1067\/j.cpradiol.2024.07.011","article-title":"Computed Tomography (CT) Derived Radiomics to Predict Post-Operative Disease Recurrence in Gastric Cancer: A Systematic Review and Meta-Analysis","volume":"53","author":"Temperley","year":"2024","journal-title":"Curr. Probl. Diagn. Radiol."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.ccell.2018.06.004","article-title":"Radiologic and Genomic Evolution of Individual Metastases during HER2 Blockade in Colorectal Cancer","volume":"34","author":"Choi","year":"2018","journal-title":"Cancer Cell"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s12672-025-01834-7","article-title":"Single Cell Transcriptomic Analysis Reveals Tumor Immune Infiltration by Macrophage Cells Gene Signature in Lung Adenocarcinoma","volume":"16","author":"Guo","year":"2025","journal-title":"Discov. Oncol."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.eururo.2020.05.009","article-title":"Transcriptomic Heterogeneity of Gleason Grade Group 5 Prostate Cancer","volume":"78","author":"Spratt","year":"2020","journal-title":"Eur. Urol."},{"key":"ref_181","first-page":"114048","article-title":"New Insights into the Prognostic Value of Genomic Classification in Synovial Sarcoma","volume":"203","author":"Gibault","year":"2024","journal-title":"Eur. J. Cancer"},{"key":"ref_182","first-page":"PO1800410","article-title":"Radiogenomics Analysis of Intratumor Heterogeneity in a Patient with High-Grade Serous Ovarian Cancer","volume":"3","author":"Weigelt","year":"2019","journal-title":"JCO Precis. Oncol."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s13244-024-01614-x","article-title":"CT and MRI Radiomics of Bone and Soft-Tissue Sarcomas: An Updated Systematic Review of Reproducibility and Validation Strategies","volume":"15","author":"Gitto","year":"2024","journal-title":"Insights Imaging"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.ebiom.2019.08.059","article-title":"Tumor Grading of Soft Tissue Sarcomas Using MRI-Based Radiomics","volume":"48","author":"Peeken","year":"2019","journal-title":"EBioMedicine"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1158\/0008-5472.CAN-21-2843","article-title":"Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis","volume":"82","author":"Xie","year":"2022","journal-title":"Cancer Res."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"e200157","DOI":"10.1148\/rycan.2021200157","article-title":"Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA","volume":"3","author":"Lafata","year":"2021","journal-title":"Radiol. Imaging Cancer"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"e220153","DOI":"10.1148\/rycan.220153","article-title":"Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know","volume":"5","author":"Klug","year":"2023","journal-title":"Radiol. Imaging Cancer"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"654","DOI":"10.2214\/AJR.11.7824","article-title":"Radiogenomic Analysis of Breast Cancer Using MRI: A Preliminary Study to Define the Landscape","volume":"199","author":"Yamamoto","year":"2012","journal-title":"Am. J. Roentgenol."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"4861","DOI":"10.1038\/s41467-020-18703-2","article-title":"Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer","volume":"11","author":"Fan","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/s41413-023-00246-z","article-title":"Managing the Immune Microenvironment of Osteosarcoma: The Outlook for Osteosarcoma Treatment","volume":"11","author":"Tian","year":"2023","journal-title":"Bone Res."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s10238-024-01530-w","article-title":"Development of a Prognostic Model for Osteosarcoma Based on Macrophage Polarization-Related Genes Using Machine Learning: Implications for Personalized Therapy","volume":"25","author":"Zeng","year":"2025","journal-title":"Clin. Exp. Med."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1007\/s11999-016-4851-y","article-title":"A Novel System for the Surgical Staging of Primary High-Grade Osteosarcoma: The Birmingham Classification","volume":"475","author":"Jeys","year":"2017","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1002\/cncr.21724","article-title":"Prognostic Factors for Osteosarcoma of the Extremity Treated with Neoadjuvant Chemotherapy: 15-Year Experience in 789 Patients Treated at a Single Institution","volume":"106","author":"Bacci","year":"2006","journal-title":"Cancer"},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1200\/JCO.2002.20.3.776","article-title":"Prognostic Factors in High-Grade Osteosarcoma of the Extremities or Trunk: An Analysis of 1,702 Patients Treated on Neoadjuvant Cooperative Osteosarcoma Study Group Protocols","volume":"20","author":"Bielack","year":"2002","journal-title":"J. Clin. Oncol."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1023\/A:1011636912674","article-title":"Predictive Factors of Disease-Free Survival for Non-Metastatic Osteosarcoma of the Extremity: An. Analysis of 300 Patients Treated at the Rizzoli Institute","volume":"12","author":"Ferrari","year":"2001","journal-title":"Ann. Oncol."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"e27792","DOI":"10.1002\/pbc.27792","article-title":"Is There a Role for Chemotherapy after Local Relapse in High-Grade Osteosarcoma?","volume":"66","author":"Palmerini","year":"2019","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ejca.2018.11.027","article-title":"Survival and Prognosis with Osteosarcoma: Outcomes in More than 2000 Patients in the EURAMOS-1 (European and American Osteosarcoma Study) Cohort","volume":"109","author":"Smeland","year":"2019","journal-title":"Eur. J. Cancer"},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1093\/annonc\/mdr491","article-title":"Survival from High-Grade Localised Extremity Osteosarcoma: Combined Results and Prognostic Factors from Three European Osteosarcoma Intergroup Randomised Controlled Trials","volume":"23","author":"Whelan","year":"2012","journal-title":"Ann. Oncol."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1016\/j.annonc.2021.08.1995","article-title":"ESMO Guidelines Committee, EURACAN, GENTURIS, and ERN PaedCan. Bone Sarcomas: ESMO\u2013EURACAN\u2013GENTURIS\u2013ERN PaedCan Clinical Practice Guideline for Diagnosis, Treatment and Follow-Up","volume":"32","author":"Strauss","year":"2021","journal-title":"Ann. Oncol."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"3029","DOI":"10.1200\/JCO.2014.59.4895","article-title":"Osteosarcoma: Current Treatment and a Collaborative Pathway to Success","volume":"33","author":"Isakoff","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"ref_201","first-page":"155","article-title":"Chemotherapy in Head and Neck Osteosarcoma: A Retrospective Analysis of 27 Patients from a Single Institution","volume":"74","author":"Chen","year":"2017","journal-title":"Oral Oncol."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"102697","DOI":"10.1016\/j.eclinm.2024.102697","article-title":"Tumor-Informed Deep Sequencing of ctDNA Detects Minimal Residual Disease and Predicts Relapse in Osteosarcoma","volume":"73","author":"Fu","year":"2024","journal-title":"EClinicalMedicine"},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1056\/NEJMra2103423","article-title":"New Horizons in the Treatment of Osteosarcoma","volume":"385","author":"Meltzer","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Lilienthal, I., and Herold, N. (2020). Targeting Molecular Mechanisms Underlying Treatment Efficacy and Resistance in Osteosarcoma: A Review of Current and Future Strategies. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21186885"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"e28352","DOI":"10.1002\/pbc.28352","article-title":"Osteosarcoma","volume":"68","author":"Eaton","year":"2021","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"vii320","DOI":"10.1093\/annonc\/mdq276","article-title":"Osteosarcoma","volume":"21","author":"Ritter","year":"2010","journal-title":"Ann. Oncol."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1038\/nrc3838","article-title":"Translational Biology of Osteosarcoma","volume":"14","author":"Kansara","year":"2014","journal-title":"Nat. Rev. Cancer"},{"key":"ref_208","unstructured":"File:SVM explain.png (2025, May 20). Wikimedia Commons. Published 22 February 2022. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:SVM_explain.png&oldid=631507021."},{"key":"ref_209","unstructured":"File:Random forest explain.png (2025, May 20). Wikimedia Commons. Published 20 November 2021. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:Random_forest_explain.png&oldid=608403035."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.ocl.2015.08.022","article-title":"Update on Survival in Osteosarcoma","volume":"47","author":"Anderson","year":"2016","journal-title":"Orthop. Clin. N. Am."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1586\/14737140.2016.1168697","article-title":"Metastatic Osteosarcoma: A Challenging Multidisciplinary Treatment","volume":"16","author":"Meazza","year":"2016","journal-title":"Expert. Rev. Anticancer Ther."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/14737140.2018.1413939","article-title":"Current and Future Therapeutic Approaches for Osteosarcoma","volume":"18","author":"Harrison","year":"2018","journal-title":"Expert. Rev. Anticancer Ther."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"357","DOI":"10.2217\/fon-2016-0261","article-title":"Novel Insights and Therapeutic Interventions for Pediatric Osteosarcoma","volume":"13","author":"Kager","year":"2017","journal-title":"Future Oncol."},{"key":"ref_214","first-page":"89","article-title":"Comprehensive Analysis of Published Phase I\/II Clinical Trials for Osteosarcoma and Ewing Sarcoma","volume":"65","author":"Bhosale","year":"2016","journal-title":"Eur. J. Cancer"},{"key":"ref_215","first-page":"1748","article-title":"Outcome of Patients with Recurrent Osteosarcoma Enrolled in Seven Phase II Trials through Children\u2019s Cancer Group, Pediatric Oncology Group, and Children\u2019s Oncology Group","volume":"63","author":"Lagmay","year":"2016","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_216","unstructured":"(2025, May 20). \u201cFile:Feature Learning Diagram.png.\u201d Wikimedia Commons. 9 October 2022. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:Feature_Learning_Diagram.png&oldid=695252798."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ejca.2017.01.005","article-title":"Phase-II Trials in Osteosarcoma Recurrences: A Systematic Review of Past Experience","volume":"75","author":"Omer","year":"2017","journal-title":"Eur. J. Cancer"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/S1470-2045(14)71186-6","article-title":"Osteosarcoma: Better Treatment through Better Trial Design","volume":"16","author":"Benjamin","year":"2015","journal-title":"Lancet Oncol."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1200\/JCO.2008.14.0095","article-title":"Children\u2019s Oncology Group Osteosarcoma: The Addition of Muramyl Tripeptide to Chemotherapy Improves Overall Survival\u2014AReport from the Children\u2019s Oncology Group","volume":"26","author":"Meyers","year":"2008","journal-title":"J. Clin. Oncol."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1200\/JCO.2014.59.5256","article-title":"Ewing Sarcoma: Current Management and Future Approaches through Collaboration","volume":"33","author":"Gaspar","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"4148","DOI":"10.1200\/JCO.2011.41.5703","article-title":"Children\u2019s Oncology Group Randomized Controlled Trial of Interval-Compressed Chemotherapy for the Treatment of Localized Ewing Sarcoma: AReport from the Children\u2019s Oncology Group","volume":"30","author":"Womer","year":"2012","journal-title":"J. Clin. Oncol."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"2536","DOI":"10.1200\/JCO.2008.19.1478","article-title":"Dose-Intensified Compared with Standard Chemotherapy for Nonmetastatic Ewing Sarcoma Family of Tumors: A Children\u2019s Oncology Group Study","volume":"27","author":"Granowetter","year":"2009","journal-title":"J. Clin. Oncol."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1056\/NEJMoa020890","article-title":"Addition of Ifosfamide and Etoposide to Standard Chemotherapy for Ewing\u2019s Sarcoma and Primitive Neuroectodermal Tumor of Bone","volume":"348","author":"Grier","year":"2003","journal-title":"N. Engl. J. Med."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"iv79","DOI":"10.1093\/annonc\/mdy310","article-title":"Bone Sarcomas: ESMO-PaedCan-EURACAN Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Up","volume":"29","author":"Casali","year":"2018","journal-title":"Ann. Oncol."},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1186\/s13018-025-05705-3","article-title":"Virtual Reality-Enhanced Rehabilitation for Improving Musculoskeletal Function and Recovery after Trauma","volume":"20","author":"Paladugu","year":"2025","journal-title":"J. Orthop. Surg. Res."},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"3108","DOI":"10.1200\/JCO.2000.18.17.3108","article-title":"Prognostic Factors in Ewing\u2019s Sarcoma of Bone: Analysis of 975 Patients from the European Intergroup Cooperative Ewing\u2019s Sarcoma Study Group","volume":"18","author":"Cotterill","year":"2000","journal-title":"J. Clin. Oncol."},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1054\/bjoc.2001.2150","article-title":"French Society of Paediatric Oncology Prognostic Factors in Localized Ewing\u2019s Tumours Peripheral Neuroectodermal Tumours: The Third Study of the French Society of Paediatric Oncology (EW88 Study)","volume":"85","author":"Oberlin","year":"2001","journal-title":"Br. J. Cancer"},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1002\/cncr.22821","article-title":"Analysis of Prognostic Factors in Ewing Sarcoma Family of Tumors: Review of St. Jude Children\u2019s Research Hospital Studies","volume":"110","author":"Liu","year":"2007","journal-title":"Cancer"},{"key":"ref_229","unstructured":"(2025, May 20). Averaged Chest Radiographs for all Participants and Heatmap of Regions\u2019 Association with Aging (Saliency Maps from the External Test Dataset) [Internet]. Wikimedia Commons. 21 September 2024. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?title=File:Averaged_chest_radiographs_for_all_participants_and_heatmap_of_regions%27_association_with_aging_(saliency_maps_from_the_external_test_dataset).jpg&oldid=926962397."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.1200\/JCO.2009.22.9864","article-title":"Primary Disseminated Multifocal Ewing Sarcoma: Results of the Euro-EWING 99 Trial","volume":"28","author":"Ladenstein","year":"2010","journal-title":"J. Clin. Oncol."},{"key":"ref_231","first-page":"3","article-title":"Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches","volume":"58","author":"Fritz","year":"2023","journal-title":"Investig. Radiol."},{"key":"ref_232","doi-asserted-by":"crossref","unstructured":"Wang, S., Sun, M., Sun, J., Wang, Q., Wang, G., Wang, X., Meng, X., Wang, Z., and Yu, H. (2024). Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics. Comput. Biol. Med., 175.","DOI":"10.1016\/j.compbiomed.2024.108502"},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"e232834","DOI":"10.1148\/radiol.232834","article-title":"Enhanced CT and MRI Focal Bone Tumor Classification with Machine Learning\u2013based Stratification: A Multicenter Retrospective Study","volume":"315","author":"Lemore","year":"2025","journal-title":"Radiology"},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/s41698-025-00855-3","article-title":"Deep learning models in classifying primary bone tumors and bone infections based on radiographs","volume":"9","author":"Wang","year":"2025","journal-title":"npj Precis. Oncol."},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1148\/radiol.2021204531","article-title":"Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs","volume":"301","author":"Wilhelm","year":"2021","journal-title":"Radiology"},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1002\/pbc.21618","article-title":"Prognostic Factors for Patients with Ewing Sarcoma (EWS) at First Recurrence Following Multi-Modality Therapy: A Report from the Children\u2019s Oncology Group","volume":"51","author":"Leavey","year":"2008","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"4354","DOI":"10.1200\/JCO.2005.05.105","article-title":"Survival after Recurrence of Ewing\u2019s Sarcoma Family of Tumors","volume":"23","author":"Barker","year":"2005","journal-title":"J. Clin. Oncol."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1002\/pbc.23040","article-title":"Risk of Recurrence and Survival after Relapse in Patients with Ewing Sarcoma","volume":"57","author":"Stahl","year":"2011","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_239","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1002\/pbc.25388","article-title":"Post-Relapse Survival in Patients with Ewing Sarcoma","volume":"62","author":"Ferrari","year":"2015","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1093\/annonc\/mdg457","article-title":"Therapy and Survival after Recurrence of Ewing\u2019s Tumors: The Rizzoli Experience in 195 Patients Treated with Adjuvant and Neoadjuvant Chemotherapy from 1979 to 1997","volume":"14","author":"Bacci","year":"2003","journal-title":"Ann. Oncol."},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1093\/annonc\/mdl108","article-title":"High Dose Chemotherapy with Bone Marrow or Peripheral Stem Cell Rescue Is an Effective Treatment Option for Patients with Relapsed or Progressive Ewing\u2019s Sarcoma Family of Tumours","volume":"17","author":"McTiernan","year":"2006","journal-title":"Ann. Oncol."},{"key":"ref_242","first-page":"9546","article-title":"Phase II Study of Oral Capecitabine in Children with Relapsed\/Refractory Ewing Sarcoma or Osteosarcoma: A Report from the Children\u2019s Oncology Group (COG)","volume":"28","author":"Gardner","year":"2010","journal-title":"J. Clin. Oncol."},{"key":"ref_243","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1634\/theoncologist.2010-0265","article-title":"Phase II Study of Sequential Gemcitabine Followed by Docetaxel for Recurrent Ewing Sarcoma, Osteosarcoma, or Unresectable or Locally Recurrent Chondrosarcoma: Results of Sarcoma Alliance for Research Through Collaboration Study 003","volume":"17","author":"Fox","year":"2012","journal-title":"Oncologist"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1002\/pbc.22206","article-title":"Irinotecan and Temozolomide for Ewing Sarcoma: The Memorial Sloan-Kettering Experience","volume":"53","author":"Casey","year":"2009","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"e27066","DOI":"10.1002\/pbc.27066","article-title":"A Phase 1 Study of Eribulin Mesylate (E7389), a Novel Microtubule-Targeting Chemotherapeutic Agent, in Children with Refractory or Recurrent Solid Tumors: A Children\u2019s Oncology Group Phase 1 Consortium Study (ADVL1314)","volume":"65","author":"Schafer","year":"2018","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1093\/annonc\/mdr151","article-title":"A Phase II Trial of Sorafenib in Relapsed and Unresectable High-Grade Osteosarcoma after Failure of Standard Multimodal Therapy: An Italian Sarcoma Group Study","volume":"23","author":"Grignani","year":"2012","journal-title":"Ann. Oncol."},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/S1470-2045(18)30742-3","article-title":"Efficacy and Safety of Regorafenib in Adult Patients with Metastatic Osteosarcoma: A Non-Comparative, Randomised, Double-Blind, Placebo-Controlled, Phase 2 Study","volume":"20","author":"Duffaud","year":"2019","journal-title":"Lancet Oncol."},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1200\/JCO.18.02374","article-title":"Randomized Double-Blind Phase II Study of Regorafenib in Patients with Metastatic Osteosarcoma","volume":"37","author":"Davis","year":"2019","journal-title":"J. Clin. Oncol."},{"key":"ref_249","doi-asserted-by":"crossref","first-page":"e27227","DOI":"10.1002\/pbc.27227","article-title":"Immunotherapy for Osteosarcoma: Where Do We Go from Here?","volume":"65","author":"Wedekind","year":"2018","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_250","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1200\/JCO.2014.58.0225","article-title":"Human Epidermal Growth Factor Receptor 2 (HER2)\u2013Specific Chimeric Antigen Receptor\u2013Modified T Cells for the Immunotherapy of HER2-Positive Sarcoma","volume":"33","author":"Ahmed","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1158\/1078-0432.CCR-15-0491","article-title":"Phase I Clinical Trial of Ipilimumab in Pediatric Patients with Advanced Solid Tumors","volume":"22","author":"Merchant","year":"2016","journal-title":"Clin. Cancer Res."},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1016\/S1470-2045(17)30624-1","article-title":"Pembrolizumab in Advanced Soft-Tissue Sarcoma and Bone Sarcoma (SARC028): A Multicentre, Two-Cohort, Single-Arm, Open-Label, Phase 2 Trial","volume":"18","author":"Tawbi","year":"2017","journal-title":"Lancet Oncol."},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S1470-2045(18)30006-8","article-title":"Nivolumab with or without Ipilimumab Treatment for Metastatic Sarcoma (Alliance A091401): Two Open-Label, Non-Comparative, Randomised, Phase 2 Trials","volume":"19","author":"Mahoney","year":"2018","journal-title":"Lancet Oncol."},{"key":"ref_254","first-page":"11504","article-title":"Programmed Cell Death 1 (PD-1) Targeting in Patients with Advanced Osteosarcomas: Results from the PEMBROSARC Study","volume":"36","author":"Blay","year":"2018","journal-title":"J. Clin. Oncol."},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1007\/s00262-021-02876-w","article-title":"Pembrolizumab in Advanced Osteosarcoma: Results of a Single-Arm, Open-Label, Phase 2 Trial","volume":"70","author":"Boye","year":"2021","journal-title":"Cancer Immunol. Immunother."},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"100221","DOI":"10.1016\/j.jbo.2019.100221","article-title":"Advances in Immune Checkpoint Inhibitors for Bone Sarcoma Therapy","volume":"15","author":"Thanindratarn","year":"2019","journal-title":"J. Bone Oncol."},{"key":"ref_257","first-page":"11508","article-title":"Targeting the Tumor Microenvironment of Ewing Sarcoma with the Combination of Dinutuximab and Trabectedin: A New Immunotherapeutic Strategy","volume":"41","author":"Zhou","year":"2023","journal-title":"J. Clin. Oncol."},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1038\/cgt.2011.83","article-title":"Immunotherapy Targeting HER2 with Genetically Modified T Cells Eliminates Tumor-Initiating Cells in Osteosarcoma","volume":"19","author":"Rainusso","year":"2012","journal-title":"Cancer Gene Ther."},{"key":"ref_259","first-page":"10506","article-title":"Reduction of MDSCs with All-Trans Retinoic Acid Improves CAR Therapy Efficacy Against Sarcomas","volume":"34","author":"Long","year":"2016","journal-title":"J. Clin. Oncol."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1038\/s41586-022-04489-4","article-title":"GD2-CAR T Cell Therapy for H3K27M-Mutated Diffuse Midline Gliomas","volume":"603","author":"Majzner","year":"2022","journal-title":"Nature"},{"key":"ref_261","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1038\/s41467-020-17175-8","article-title":"Tumor Response and Endogenous Immune Reactivity Following Administration of HER2 CAR T Cells in a Child with Metastatic Rhabdomyosarcoma","volume":"11","author":"Hegde","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_262","first-page":"10507","article-title":"Administration of HER2-Chimeric Antigen Receptor (CAR) Expressing T Cells for Sarcoma Is Safe, with Preliminary Evidence of Efficacy in HER2 Low-Expressing Tumors","volume":"36","author":"Navai","year":"2018","journal-title":"J. Clin. Oncol."},{"key":"ref_263","first-page":"10529","article-title":"GD2 Chimeric Antigen Receptor T-Cell Therapy for Tyrosine Hydroxylase-Positive Stage 4 Neuroblastomas and Sarcomas","volume":"38","author":"Chulanetra","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.cancergen.2016.03.004","article-title":"Current State of Pediatric Sarcoma Biology and Opportunities for Future Discovery: A Report from the Sarcoma Translational Research Workshop","volume":"209","author":"Hingorani","year":"2016","journal-title":"Cancer Genet."},{"key":"ref_265","doi-asserted-by":"crossref","unstructured":"Sporn, K., Kumar, R., Paladugu, P., Ong, J., Sekhar, T., Vaja, S., Hage, T., Waisberg, E., Gowda, C., and Jagadeesan, R. (2025). Artificial Intelligence in Orthopedic Medical Education: A Comprehensive Review of Emerging Technologies and Their Applications. Int. Med. Educ., 4.","DOI":"10.20944\/preprints202504.0306.v1"},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"3514","DOI":"10.1002\/cncr.32351","article-title":"Provocative Questions in Osteosarcoma Research: An Update from the Children\u2019s Oncology Group","volume":"125","author":"Roberts","year":"2019","journal-title":"Cancer"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/11\/1377\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:43:09Z","timestamp":1760031789000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/11\/1377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,29]]},"references-count":266,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["diagnostics15111377"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15111377","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,29]]}}}