{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:05:52Z","timestamp":1771632352849,"version":"3.50.1"},"reference-count":116,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,13]],"date-time":"2022-02-13T00:00:00Z","timestamp":1644710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients\u2019 treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the \u201cRadiomics Quality Score\u201d and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation.<\/jats:p>","DOI":"10.3390\/jimaging8020045","type":"journal-article","created":{"date-parts":[[2022,2,13]],"date-time":"2022-02-13T21:08:43Z","timestamp":1644786523000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Radiomics of Musculoskeletal Sarcomas: A Narrative Review"],"prefix":"10.3390","volume":"8","author":[{"given":"Cristiana","family":"Fanciullo","sequence":"first","affiliation":[{"name":"Scuola di Specializzazione in Radiodiagnostica, Universit\u00e0 degli Studi di Milano, 20122 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3623-7822","authenticated-orcid":false,"given":"Salvatore","family":"Gitto","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Biomediche per la Salute, Universit\u00e0 degli Studi di Milano, via Riccardo Galeazzi 4, 20161 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0464-5431","authenticated-orcid":false,"given":"Eleonora","family":"Carlicchi","sequence":"additional","affiliation":[{"name":"Scuola di Specializzazione in Radiodiagnostica, Universit\u00e0 degli Studi di Milano, 20122 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7989-9861","authenticated-orcid":false,"given":"Domenico","family":"Albano","sequence":"additional","affiliation":[{"name":"IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy"},{"name":"Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Universit\u00e0 degli Studi di Palermo, 90127 Palermo, Italy"}]},{"given":"Carmelo","family":"Messina","sequence":"additional","affiliation":[{"name":"IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy"}]},{"given":"Luca Maria","family":"Sconfienza","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Biomediche per la Salute, Universit\u00e0 degli Studi di Milano, via Riccardo Galeazzi 4, 20161 Milan, Italy"},{"name":"IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"iv79","DOI":"10.1093\/annonc\/mdy310","article-title":"Bone Sarcomas: ESMO\u2013PaedCan\u2013EURACAN Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Up","volume":"29","author":"Casali","year":"2018","journal-title":"Ann. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"iv51","DOI":"10.1093\/annonc\/mdy096","article-title":"Soft Tissue and Visceral Sarcomas: ESMO\u2013EURACAN Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Up","volume":"29","author":"Casali","year":"2018","journal-title":"Ann. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"026","DOI":"10.1055\/s-0038-1675551","article-title":"Differential Diagnosis of Spine Tumors: My Favorite Mistake","volume":"23","author":"Albano","year":"2019","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5152\/dir.2019.19321","article-title":"Radiomics with Artificial Intelligence: A Practical Guide for Beginners","volume":"25","author":"Kocak","year":"2019","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1148\/rg.2017170056","article-title":"CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges","volume":"37","author":"Lubner","year":"2017","journal-title":"Radiographics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1177\/107327480501200103","article-title":"Diagnostic Imaging Update: Soft Tissue Sarcomas","volume":"12","author":"Knapp","year":"2005","journal-title":"Cancer Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1148\/radiology.185.2.1410377","article-title":"Diagnosis of Soft-Tissue Masses with MR Imaging: Can Benign Masses Be Differentiated from Malignant Ones?","volume":"185","author":"Crim","year":"1992","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e831","DOI":"10.1259\/bjr\/27487871","article-title":"MRI to Differentiate Benign from Malignant Soft-Tissue Tumours of the Extremities: A Simplified Systematic Imaging Approach Using Depth, Size and Heterogeneity of Signal Intensity","volume":"85","author":"Chung","year":"2012","journal-title":"Br. J. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.2214\/ajr.164.5.7717231","article-title":"MR Imaging of Soft-Tissue Masses: Diagnostic Efficacy and Value of Distinguishing between Benign and Malignant Lesions","volume":"164","author":"Moulton","year":"1995","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1007\/s00330-004-2431-0","article-title":"Accuracy of MRI in Characterization of Soft Tissue Tumors and Tumor-like Lesions. A Prospective Study in 548 Patients","volume":"14","author":"Gielen","year":"2004","journal-title":"Eur. Radiol."},{"key":"ref_12","unstructured":"(2020). The WHO Classification of Tumours Editorial Board. WHO Classification of Tumours Soft Tissue and Bone Tumours, IARC Press. [5th ed.]."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"575","DOI":"10.2214\/ajr.175.3.1750575","article-title":"Radiologic Evaluation of Soft-Tissue Masses","volume":"175","author":"Kransdorf","year":"2000","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1055\/s-2008-1032948","article-title":"Statistische Analyse von Kernspinresonanzparametern Zur Malignit\u00e4ts-Prognose Bei 141 Weichteiltumoren","volume":"156","author":"Ramon","year":"1992","journal-title":"Rofo"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s003300050037","article-title":"Magnetic Resonance Imaging of Soft Tissue Tumors","volume":"10","author":"Vandevenne","year":"2000","journal-title":"Eur. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.1007\/s00330-006-0267-5","article-title":"Soft Tissue Masses with \u201cCyst-like\u201d Appearance on MR Imaging: Distinction of Benign and Malignant Lesions","volume":"16","author":"Harish","year":"2006","journal-title":"Eur. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s13244-012-0196-6","article-title":"Assessment of Tumor Heterogeneity: An Emerging Imaging Tool for Clinical Practice?","volume":"3","author":"Davnall","year":"2012","journal-title":"Insights Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1102\/1470-7330.2013.0015","article-title":"Quantifying Tumour Heterogeneity with CT","volume":"13","author":"Ganeshan","year":"2013","journal-title":"Cancer Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1002\/jmri.22095","article-title":"Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft-Tissue Tumors in T1-MRI Images","volume":"31","author":"Juntu","year":"2010","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1002\/jmri.27111","article-title":"Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study","volume":"52","author":"Wang","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1002\/jmri.26818","article-title":"Radiomics Nomogram for Differentiating between Benign and Malignant Soft-tissue Masses of the Extremities","volume":"51","author":"Wang","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"287","DOI":"10.3109\/17453678109050105","article-title":"A Consecutive 7-Year Series of 1331 Benign Soft Tissue Tumours: Clinicopathologic Data. Comparison with Sarcomas","volume":"52","year":"1981","journal-title":"Acta Orthop. Scand."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1053\/adpa.2000.8133","article-title":"Liposarcoma: New Entities and Evolving Concepts","volume":"4","year":"2000","journal-title":"Ann. Diagn. Pathol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nagano, S., Yokouchi, M., Setoguchi, T., Ishidou, Y., Sasaki, H., Shimada, H., and Komiya, S. (2015). Differentiation of Lipoma and Atypical Lipomatous Tumor by a Scoring System: Implication of Increased Vascularity on Pathogenesis of Liposarcoma. BMC Musculoskelet. Disord., 16.","DOI":"10.1186\/s12891-015-0491-8"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/982784","article-title":"Can Experienced Observers Differentiate between Lipoma and Well-Differentiated Liposarcoma Using Only MRI?","volume":"2013","author":"Griffin","year":"2013","journal-title":"Sarcoma"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1016\/j.acra.2014.04.005","article-title":"Differentiation of Lipoma From Liposarcoma on MRI Using Texture and Shape Analysis","volume":"21","author":"Thornhill","year":"2014","journal-title":"Acad. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/7163453","article-title":"Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists","volume":"2020","author":"Malinauskaite","year":"2020","journal-title":"Sarcoma"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s00428-009-0815-x","article-title":"Well-Differentiated and Dedifferentiated Liposarcomas","volume":"456","author":"Coindre","year":"2010","journal-title":"Virchows Arch."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1038\/modpathol.2012.90","article-title":"Sensitivity of MDM2 Amplification and Unexpected Multiple Faint Alphoid 12 (Alpha 12 Satellite Sequences) Signals in Atypical Lipomatous Tumor","volume":"25","author":"Kashima","year":"2012","journal-title":"Mod. Pathol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1038\/modpathol.2008.84","article-title":"Fluorescence in Situ Hybridization for MDM2 Gene Amplification as a Diagnostic Tool in Lipomatous Neoplasms","volume":"21","author":"Weaver","year":"2008","journal-title":"Mod. Pathol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1002\/(SICI)1096-9896(200004)190:5<531::AID-PATH579>3.0.CO;2-W","article-title":"Coordinated Expression and Amplification of TheMDM2,CDK4, AndHMGI-C Genes in Atypical Lipomatous Tumours","volume":"190","author":"Doglioni","year":"2000","journal-title":"J. Pathol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1901896","DOI":"10.1155\/2018\/1901896","article-title":"The Value of MRI in Distinguishing Subtypes of Lipomatous Extremity Tumors Needs Reassessment in the Era of MDM2 and CDK4 Testing","volume":"2018","author":"Ryan","year":"2018","journal-title":"Sarcoma"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s00256-012-1517-z","article-title":"MRI Characteristics of Lipoma and Atypical Lipomatous Tumor\/Well-Differentiated Liposarcoma: Retrospective Comparison with Histology and MDM2 Gene Amplification","volume":"42","author":"Brisson","year":"2013","journal-title":"Skeletal Radiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s00256-020-03372-5","article-title":"Qualitative Evaluation of MRI Features of Lipoma and Atypical Lipomatous Tumor: Results from a Multicenter Study","volume":"49","author":"Nardo","year":"2020","journal-title":"Skeletal Radiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1007\/s00256-020-03454-4","article-title":"Pilot Study to Differentiate Lipoma from Atypical Lipomatous Tumour\/Well-Differentiated Liposarcoma Using MR Radiomics-Based Texture Analysis","volume":"49","author":"Pressney","year":"2020","journal-title":"Skeletal Radiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s00330-017-5014-6","article-title":"Diagnostic Value of MRI-Based 3D Texture Analysis for Tissue Characterisation and Discrimination of Low-Grade Chondrosarcoma from Enchondroma: A Pilot Study","volume":"28","author":"Lisson","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3480","DOI":"10.1007\/s00330-015-3764-6","article-title":"Prevalence of Cartilaginous Tumours as an Incidental Finding on MRI of the Knee","volume":"25","author":"Stomp","year":"2015","journal-title":"Eur. Radiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.2106\/00004623-200710000-00003","article-title":"Reliability of Histopathologic and Radiologic Grading of Cartilaginous Neoplasms in Long Bones","volume":"89","author":"Jones","year":"2007","journal-title":"J. Bone Joint Surg. Am."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1007\/s00330-018-5730-6","article-title":"Comparison of Radiomics Machine-Learning Classifiers and Feature Selection for Differentiation of Sacral Chordoma and Sacral Giant Cell Tumour Based on 3D Computed Tomography Features","volume":"29","author":"Yin","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_40","first-page":"1","article-title":"Differentiation of Pelvic Osteosarcoma and Ewing Sarcoma Using Radiomic Analysis Based on T2-Weighted Images and Contrast-Enhanced T1-Weighted Images","volume":"2020","author":"Dai","year":"2020","journal-title":"Biomed Res. Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ejrad.2019.07.006","article-title":"Radiomic Analysis of Multiparametric Magnetic Resonance Imaging for Differentiating Skull Base Chordoma and Chondrosarcoma","volume":"118","author":"Li","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109586","DOI":"10.1016\/j.ejrad.2021.109586","article-title":"Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study","volume":"137","author":"Chianca","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1002\/1097-0142(20010515)91:10<1914::AID-CNCR1214>3.0.CO;2-3","article-title":"Predictive Value of Grade for Metastasis Development in the Main Histologic Types of Adult Soft Tissue Sarcomas","volume":"91","author":"Coindre","year":"2001","journal-title":"Cancer"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2436","DOI":"10.1093\/annonc\/mdq238","article-title":"Effect of Adjuvant Chemotherapy on Survival in FNCLCC Grade 3 Soft Tissue Sarcomas: A Multivariate Analysis of the French Sarcoma Group Database","volume":"21","author":"Italiano","year":"2010","journal-title":"Ann. Oncol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s00292-016-0184-6","article-title":"Grading von Weichgewebe- und Knochensarkomen","volume":"37","author":"Petersen","year":"2016","journal-title":"Pathologe"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.5858\/2006-130-1448-GOSTSR","article-title":"Grading of Soft Tissue Sarcomas: Review and Update","volume":"130","author":"Coindre","year":"2006","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1002\/jso.21600","article-title":"The Role of Core Needle Biopsy in the Diagnosis of Suspected Soft Tissue Tumours","volume":"102","author":"Strauss","year":"2010","journal-title":"J. Surg. Oncol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1148\/radiol.14131871","article-title":"Can MR Imaging Be Used to Predict Tumor Grade in Soft-Tissue Sarcoma?","volume":"272","author":"Zhao","year":"2014","journal-title":"Radiology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1038\/bjc.2014.512","article-title":"Improving Tumour Heterogeneity MRI Assessment with Histograms","volume":"111","author":"Just","year":"2014","journal-title":"Br. J. Cancer"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1002\/jmri.25791","article-title":"Radiomic Analysis of Soft Tissues Sarcomas Can Distinguish Intermediate from High-Grade Lesions","volume":"47","author":"Corino","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ejrad.2019.07.028","article-title":"Distinguishing Soft Tissue Sarcomas of Different Histologic Grades Based on Quantitative MR Assessment of Intratumoral Heterogeneity","volume":"118","author":"Xiang","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.acra.2018.09.025","article-title":"Soft Tissue Sarcomas: Preoperative Predictive Histopathological Grading Based on Radiomics of MRI","volume":"26","author":"Zhang","year":"2019","journal-title":"Acad. Radiol."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.suronc.2018.05.009","article-title":"Incidence, Outcomes and Prognostic Factors during 25 Years of Treatment of Chondrosarcomas","volume":"27","author":"Ho","year":"2018","journal-title":"Surg. Oncol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3765","DOI":"10.2147\/CMAR.S178768","article-title":"The Impact of Biopsy Sampling Errors and the Quality of Surgical Margins on Local Recurrence and Survival in Chondrosarcoma","volume":"10","author":"Hodel","year":"2018","journal-title":"Cancer Manag. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3288","DOI":"10.1002\/cncr.32404","article-title":"(Hans). Radiologic Differentiation of Enchondromas, Atypical Cartilaginous Tumors, and High-grade Chondrosarcomas\u2014Improving Tumor-specific Treatment: A Paradigm in Transit?","volume":"125","author":"Kroon","year":"2019","journal-title":"Cancer"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/s00330-013-3003-y","article-title":"MRI Differentiation of Low-Grade from High-Grade Appendicular Chondrosarcoma","volume":"24","author":"Douis","year":"2014","journal-title":"Eur. Radiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1007\/s00256-015-2123-7","article-title":"Is There a Role for Diffusion-Weighted MRI (DWI) in the Diagnosis of Central Cartilage Tumors?","volume":"44","author":"Douis","year":"2015","journal-title":"Skeletal Radiol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1097\/RLI.0000000000000486","article-title":"Magnetic Resonance Imaging\u2013Based Grading of Cartilaginous Bone Tumors","volume":"53","author":"Fritz","year":"2018","journal-title":"Invest. Radiol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1007\/s00234-019-02266-1","article-title":"Prediction of High Proliferative Index in Pituitary Macroadenomas Using MRI-Based Radiomics and Machine Learning","volume":"61","author":"Ugga","year":"2019","journal-title":"Neuroradiology"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"109043","DOI":"10.1016\/j.ejrad.2020.109043","article-title":"MRI Radiomics-Based Machine-Learning Classification of Bone Chondrosarcoma","volume":"128","author":"Gitto","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103407","DOI":"10.1016\/j.ebiom.2021.103407","article-title":"CT Radiomics-Based Machine Learning Classification of Atypical Cartilaginous Tumours and Appendicular Chondrosarcomas","volume":"68","author":"Gitto","year":"2021","journal-title":"EBioMedicine"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"103757","DOI":"10.1016\/j.ebiom.2021.103757","article-title":"MRI Radiomics-Based Machine Learning Classification of Atypical Cartilaginous Tumour and Grade II Chondrosarcoma of Long Bones","volume":"75","author":"Gitto","year":"2022","journal-title":"EBioMedicine"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/S1470-2045(17)30334-0","article-title":"Histotype-Tailored Neoadjuvant Chemotherapy versus Standard Chemotherapy in Patients with High-Risk Soft-Tissue Sarcomas (ISG-STS 1001): An International, Open-Label, Randomised, Controlled, Phase 3, Multicentre Trial","volume":"18","author":"Gronchi","year":"2017","journal-title":"Lancet Oncol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S1470-2045(10)70071-1","article-title":"Neo-Adjuvant Chemotherapy Alone or with Regional Hyperthermia for Localised High-Risk Soft-Tissue Sarcoma: A Randomised Phase 3 Multicentre Study","volume":"11","author":"Issels","year":"2010","journal-title":"Lancet Oncol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejca.2016.09.030","article-title":"(Neo)Adjuvant Treatment in Localised Soft Tissue Sarcoma: The Unsolved Affair","volume":"70","author":"Saponara","year":"2017","journal-title":"Eur. J. Cancer"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1148\/radiol.2512081403","article-title":"High-Grade Soft-Tissue Sarcomas: Tumor Response Assessment--Pilot Study to Assess the Correlation between Radiologic and Pathologic Response by Using RECIST and Choi Criteria","volume":"251","author":"Stacchiotti","year":"2009","journal-title":"Radiology"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5857","DOI":"10.1002\/cncr.27624","article-title":"Tumor Response Assessment by Modified Choi Criteria in Localized High-Risk Soft Tissue Sarcoma Treated with Chemotherapy","volume":"118","author":"Stacchiotti","year":"2012","journal-title":"Cancer"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s00330-018-5635-4","article-title":"High-Grade Soft-Tissue Sarcoma: Optimizing Injection Improves MRI Evaluation of Tumor Response","volume":"29","author":"Cornelis","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2856","DOI":"10.1158\/1078-0432.CCR-08-2537","article-title":"FDG-PET\/CT Imaging Predicts Histopathologic Treatment Responses after the Initial Cycle of Neoadjuvant Chemotherapy in High-Grade Soft-Tissue Sarcomas","volume":"15","author":"Benz","year":"2009","journal-title":"Clin. Cancer Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.2967\/jnumed.108.053694","article-title":"Combined Assessment of Metabolic and Volumetric Changes for Assessment of Tumor Response in Patients with Soft-Tissue Sarcomas","volume":"49","author":"Benz","year":"2008","journal-title":"J. Nucl. Med."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The Bridge between Medical Imaging and Personalized Medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"S20","DOI":"10.1016\/S0167-8140(16)30042-1","article-title":"Early Variation of FDG-PET Radiomics Features in NSCLC Is Related to Overall Survival\u2014The \u201cDelta Radiomics\u201d Concept","volume":"118","author":"Carvalho","year":"2016","journal-title":"Radiother. Oncol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1038\/s41598-017-00665-z","article-title":"Delta-Radiomics Features for the Prediction of Patient Outcomes in Non\u2013Small Cell Lung Cancer","volume":"7","author":"Fave","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1002\/jmri.26589","article-title":"T 2-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_76","doi-asserted-by":"crossref","first-page":"175006","DOI":"10.1088\/1361-6560\/ab9e58","article-title":"Treatment Effect Prediction for Sarcoma Patients Treated with Preoperative Radiotherapy Using Radiomics Features from Longitudinal Diffusion-Weighted MRIs","volume":"65","author":"Gao","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.1016\/S0959-8049(01)00229-5","article-title":"Neoadjuvant Chemotherapy for Osteosarcoma of the Extremity","volume":"37","author":"Bacci","year":"2001","journal-title":"Eur. J. Cancer"},{"key":"ref_78","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_79","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1200\/JCO.1994.12.2.423","article-title":"Prognostic Factors in Osteosarcoma: A Critical Review","volume":"12","author":"Davis","year":"1994","journal-title":"J. Clin. Oncol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1309\/H0D4VD760NH6N1R6","article-title":"Treatment Effects in Pediatric Soft Tissue and Bone Tumors","volume":"123","author":"Coffin","year":"2005","journal-title":"Am. J. Clin. Pathol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s00259-011-1936-4","article-title":"Prediction of Tumour Necrosis Fractions Using Metabolic and Volumetric 18F-FDG PET\/CT Indices, after One Course and at the Completion of Neoadjuvant Chemotherapy, in Children and Young Adults with Osteosarcoma","volume":"39","author":"Im","year":"2012","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.2967\/jnumed.112.115964","article-title":"Combination of 18 F-FDG PET\/CT and Diffusion-Weighted MR Imaging as a Predictor of Histologic Response to Neoadjuvant Chemotherapy: Preliminary Results in Osteosarcoma","volume":"54","author":"Byun","year":"2013","journal-title":"J. Nucl. Med."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s00259-014-2746-2","article-title":"Early Response Monitoring to Neoadjuvant Chemotherapy in Osteosarcoma Using Sequential 18 F-FDG PET\/CT and MRI","volume":"41","author":"Byun","year":"2014","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.2967\/jnumed.109.063602","article-title":"Prediction Model of Chemotherapy Response in Osteosarcoma by 18 F-FDG PET and MRI","volume":"50","author":"Cheon","year":"2009","journal-title":"J. Nucl. Med."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s40644-019-0283-8","article-title":"A Delta-Radiomics Model for Preoperative Evaluation of Neoadjuvant Chemotherapy Response in High-Grade Osteosarcoma","volume":"20","author":"Lin","year":"2020","journal-title":"Cancer Imaging"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"3012","DOI":"10.1007\/s11999-010-1481-7","article-title":"Local Recurrence After Initial Multidisciplinary Management of Soft Tissue Sarcoma: Is There a Way Out?","volume":"468","author":"Abatzoglou","year":"2010","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1002\/jso.23541","article-title":"MRI Surveillance Following Treatment of Extremity Soft Tissue Sarcoma","volume":"109","author":"Cheney","year":"2014","journal-title":"J. Surg. Oncol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.jacr.2015.12.019","article-title":"ACR Appropriateness Criteria Follow-Up of Malignant or Aggressive Musculoskeletal Tumors","volume":"13","author":"Roberts","year":"2016","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1055\/s-0035-1569251","article-title":"Soft Tissue Tumors in Adults: ESSR-Approved Guidelines for Diagnostic Imaging","volume":"19","author":"Weber","year":"2015","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1148\/radiol.13130844","article-title":"Detection of Soft-Tissue Sarcoma Recurrence: Added Value of Functional MR Imaging Techniques at 3.0 T","volume":"271","author":"Subhawong","year":"2014","journal-title":"Radiology"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1148\/radiol.12111740","article-title":"Musculoskeletal Tumors: How to Use Anatomic, Functional, and Metabolic MR Techniques","volume":"265","author":"Fayad","year":"2012","journal-title":"Radiology"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"300","DOI":"10.2478\/raon-2019-0041","article-title":"Local Recurrence of Soft Tissue Sarcoma: A Radiomic Analysis","volume":"53","author":"Tagliafico","year":"2019","journal-title":"Radiol. Oncol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1097\/00000658-199905000-00002","article-title":"Pulmonary Metastases From Soft Tissue Sarcoma","volume":"229","author":"Billingsley","year":"1999","journal-title":"Ann. Surg."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/S1479-666X(05)80044-7","article-title":"Soft Tissue Sarcoma: Advances in Understanding and Management","volume":"3","author":"Brennan","year":"2005","journal-title":"Surgeon"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/S0011-3840(96)80013-X","article-title":"Soft Tissue Sarcomas","volume":"33","author":"Lewis","year":"1996","journal-title":"Curr. Probl. Surg."},{"key":"ref_96","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_97","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1002\/jso.23523","article-title":"Metabolic Activity Measured on PET\/CT Correlates with Clinical Outcomes in Patients with Limb and Girdle Sarcomas","volume":"109","author":"Skamene","year":"2014","journal-title":"J. Surg. Oncol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00259-002-0859-5","article-title":"Sarcoma Tumor FDG Uptake Measured by PET and Patient Outcome: A Retrospective Analysis","volume":"29","author":"Eary","year":"2002","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1200\/JCO.2005.04.063","article-title":"Osteosarcoma Relapse After Combined Modality Therapy: An Analysis of Unselected Patients in the Cooperative Osteosarcoma Study Group (COSS)","volume":"23","author":"Bielack","year":"2005","journal-title":"J. Clin. Oncol."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3892\/ol.2013.1322","article-title":"Late Post-Operative Recurrent Osteosarcoma: Three Case Reports with a Review of the Literature","volume":"6","author":"Yu","year":"2013","journal-title":"Oncol. Lett."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"109066","DOI":"10.1016\/j.ejrad.2020.109066","article-title":"Development and External Validation of an MRI-Based Radiomics Nomogram for Pretreatment Prediction for Early Relapse in Osteosarcoma: A Retrospective Multicenter Study","volume":"129","author":"Chen","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1002\/ijc.2910330108","article-title":"Soft-Tissue Sarcomas of Adults; Study of Pathological Prognostic Variables and Definition of a Histopathological Grading System","volume":"33","author":"Trojani","year":"1984","journal-title":"Int. J. Cancer"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1200\/JCO.1997.15.1.350","article-title":"Comparative Study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group Grading Systems in a Population of 410 Adult Patients with Soft Tissue Sarcoma","volume":"15","author":"Guillou","year":"1997","journal-title":"J. Clin. Oncol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1200\/JCO.1996.14.3.869","article-title":"Prognostic Factors in Adult Patients with Locally Controlled Soft Tissue Sarcoma. A Study of 546 Patients from the French Federation of Cancer Centers Sarcoma Group","volume":"14","author":"Coindre","year":"1996","journal-title":"J. Clin. Oncol."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1148\/radiol.2019181659","article-title":"Soft-Tissue Sarcomas: Assessment of MRI Features Correlating with Histologic Grade and Patient Outcome","volume":"291","author":"Marcellin","year":"2019","journal-title":"Radiology"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1093\/biostatistics\/kxi010","article-title":"Incorporation of Tumor Shape into an Assessment of Spatial Heterogeneity for Human Sarcomas Imaged with FDG-PET","volume":"6","author":"Roy","year":"2005","journal-title":"Biostatistics"},{"key":"ref_107","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_108","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.radonc.2019.01.004","article-title":"CT-Based Radiomic Features Predict Tumor Grading and Have Prognostic Value in Patients with Soft Tissue Sarcomas Treated with Neoadjuvant Radiation Therapy","volume":"135","author":"Peeken","year":"2019","journal-title":"Radiother. Oncol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"100263","DOI":"10.1016\/j.jbo.2019.100263","article-title":"Radiomics Signature Extracted from Diffusion-Weighted Magnetic Resonance Imaging Predicts Outcomes in Osteosarcoma","volume":"19","author":"Zhao","year":"2019","journal-title":"J. Bone Oncol."},{"key":"ref_110","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_111","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s13244-021-01008-3","article-title":"CT and MRI Radiomics of Bone and Soft-Tissue Sarcomas: A Systematic Review of Reproducibility and Validation Strategies","volume":"12","author":"Gitto","year":"2021","journal-title":"Insights Imaging"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"109283","DOI":"10.1016\/j.ejrad.2020.109283","article-title":"Systematic Review of Sarcomas Radiomics Studies: Bridging the Gap between Concepts and Clinical Applications?","volume":"132","author":"Fadli","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1007\/s00330-020-07221-w","article-title":"A Systematic Review of Radiomics in Osteosarcoma: Utilizing Radiomics Quality Score as a Tool Promoting Clinical Translation","volume":"31","author":"Zhong","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1007\/s10278-021-00498-3","article-title":"Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors","volume":"34","author":"Gitto","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s00256-021-03802-y","article-title":"Artificial intelligence in musculoskeletal imaging: A perspective on value propositions, clinical use, and obstacles","volume":"51","author":"Fritz","year":"2022","journal-title":"Skeletal Radiol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3233\/FI-2020-1887","article-title":"A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration","volume":"171","author":"Rundo","year":"2020","journal-title":"Fundam. Inform."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/2\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:18:36Z","timestamp":1760134716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/2\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,13]]},"references-count":116,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["jimaging8020045"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8020045","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,13]]}}}