{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:22:16Z","timestamp":1787923336993,"version":"build-2784847793"},"reference-count":125,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MOH\/NMRC, Singapore","award":["MOH-001924"],"award-info":[{"award-number":["MOH-001924"]}]},{"name":"MOH\/NMRC, Singapore","award":["CIAINV25jan-0005"],"award-info":[{"award-number":["CIAINV25jan-0005"]}]},{"award":["MOH-001924"],"award-info":[{"award-number":["MOH-001924"]}],"id":[{"id":"https:\/\/ror.org\/04x3cxs03","id-type":"ROR","asserted-by":"publisher"}]},{"award":["CIAINV25jan-0005"],"award-info":[{"award-number":["CIAINV25jan-0005"]}],"id":[{"id":"https:\/\/ror.org\/04x3cxs03","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Bone metastasis is one of the most common manifestations of advanced malignancy and a major cause of morbidity, particularly when involving the spine. Magnetic resonance imaging (MRI) plays a central role in its detection and characterisation due to its high sensitivity for bone marrow infiltration. However, bone metastases may be missed on MRI, whilst interpretation can be time-consuming and challenging. The purpose of this study is to review and summarise the present evidence for artificial intelligence (AI) applications in the detection and classification of bone metastasis on MRI. Methods: A systematic, detailed search of the main electronic medical databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov, last accessed on 1 January 2026) was undertaken in concordance with the PRISMA guidelines. Results: A total of 34 studies were included. AI applications were identified across several domains, including lesion detection, segmentation, disease classification, and predictive modelling. Deep learning approaches demonstrated strong performance for automated detection and segmentation, while radiomics-based models were frequently used for lesion differentiation and prediction tasks. Reported performance metrics were generally high, with area under the curve values commonly ranging from approximately 0.72\u20130.94, with most studies reporting AUCs exceeding 0.80 in internal validation, although substantial heterogeneity in study design, datasets, and validation strategies was observed. External validation and prospective evaluation were limited across most studies. Conclusions: Within the domain of bone metastasis, AI-based approaches have demonstrated encouraging performance and hold substantial potential to support clinical decision-making, including prognostication and prediction of treatment response. Nevertheless, further research is required to validate their clinical utility and to facilitate successful integration into routine clinical practice.<\/jats:p>","DOI":"10.3390\/cancers18172794","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:27:46Z","timestamp":1787920066000},"page":"2794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1603-5088","authenticated-orcid":false,"given":"Juncheng","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0262-3802","authenticated-orcid":false,"given":"Wilson","family":"Ong Ying Fa","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aric","family":"Lee Wei Zheng","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9297-4598","authenticated-orcid":false,"given":"Timothy Shao Ern","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Imaging, KK Women\u2019s and Children\u2019s Hospital, 100 Bukit Timah Rd, Singapore 229899, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gordan","family":"Toh Cheong Zheng","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7053-2083","authenticated-orcid":false,"given":"Ee Chin","family":"Teo","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiong Hao Jonathan","family":"Tan","sequence":"additional","affiliation":[{"name":"National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, Singapore 119228, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4538-5031","authenticated-orcid":false,"given":"Naresh","family":"Kumar","sequence":"additional","affiliation":[{"name":"National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, Singapore 119228, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-9033","authenticated-orcid":false,"given":"James T. P. D.","family":"Hallinan","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"ref_1","first-page":"321","article-title":"Bone metastases: An overview","volume":"11","author":"Macedo","year":"2017","journal-title":"Oncol. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1152\/physrev.00012.2019","article-title":"Bone metastasis: Mechanisms, therapies, and biomarkers","volume":"101","author":"Coleman","year":"2021","journal-title":"Physiol. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s10585-019-09987-w","article-title":"Metastatic dissemination patterns of different primary tumors to the spine and other bones","volume":"36","author":"Onken","year":"2019","journal-title":"Clin. Exp. Metastasis"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3347","DOI":"10.3390\/curroncol28050290","article-title":"Current Overview of Treatment for Metastatic Bone Disease","volume":"28","author":"Tsukamoto","year":"2021","journal-title":"Curr. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1097\/PPO.0000000000000717","article-title":"Bone Metastases","volume":"30","author":"Cook","year":"2024","journal-title":"Cancer J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"202","DOI":"10.4329\/wjr.v7.i8.202","article-title":"Imaging of bone metastasis: An update","volume":"7","author":"Carty","year":"2015","journal-title":"World J. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ctrv.2017.09.008","article-title":"Early identification and intervention matters: A comprehensive review of current evidence and recommendations for the monitoring of bone health in patients with cancer","volume":"61","author":"Brodowicz","year":"2017","journal-title":"Cancer Treat. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, S., Shen, Y., Li, H., Geng, T., Yan, J., Wang, L., and Sui, L. (2025). Bone metastases from endocrine cancer: Advances in diagnosis, treatment, and prevention. Front. Endocrinol., 16.","DOI":"10.3389\/fendo.2025.1669154"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ouvrard, E., Kaseb, A., Poterszman, N., Porot, C., Somme, F., and Imperiale, A. (2024). Nuclear medicine imaging for bone metastases assessment: What else besides bone scintigraphy in the era of personalized medicine?. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1320574"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Conforti, P.M., D\u2019acunto, M., and Russo, P. (2022). Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra. Sensors, 22.","DOI":"10.3390\/s22197492"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Oprea-Lager, D.E., Cysouw, M.C., Boellaard, R., Deroose, C.M., de Geus-Oei, L.-F., Lopci, E., Bidaut, L., Herrmann, K., Fournier, L.S., and B\u00e4uerle, T. (2021). Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.772530"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"572","DOI":"10.4103\/singaporemedj.SMJ-2021-215","article-title":"Magnetic resonance imaging of spinal epidural lesions","volume":"64","author":"Lee","year":"2023","journal-title":"Singap. Med. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110372","DOI":"10.1016\/j.compbiomed.2025.110372","article-title":"Artificial intelligence in bone metastasis analysis: Current advancements, opportunities and challenges","volume":"194","author":"Afnouch","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_14","first-page":"18","article-title":"Magnetic Resonance Imaging correlates of benign and malignant alterations of the spinal bone marrow","volume":"89","author":"Caranci","year":"2018","journal-title":"Acta Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100745","DOI":"10.1016\/j.jbo.2026.100745","article-title":"Clinically aligned whole-body MRI segmentation of skeletal metastases via Supervised Anatomical Pretraining","volume":"57","author":"Wuts","year":"2026","journal-title":"J. Bone Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1302\/2058-5241.2.170006","article-title":"Diagnostic algorithm, prognostic factors and surgical treatment of metastatic cancer diseases of the long bones and spine","volume":"2","author":"Antal","year":"2017","journal-title":"EFORT Open Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1093\/annonc\/mdu558","article-title":"Assessing response to treatment of bone metastases from breast cancer: What should be the standard of care?","volume":"26","author":"Woolf","year":"2014","journal-title":"Ann. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4703","DOI":"10.3390\/curroncol29070373","article-title":"The Prediction of Survival after Surgical Management of Bone Metastases of the Extremities\u2014A Comparison of Prognostic Models","volume":"29","author":"Soh","year":"2022","journal-title":"Curr. Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bulut, H., Demir\u00f6z, S., Kanay, E., Ozkan, K., and Errani, C. (2026). Artificial Intelligence and Machine Learning in Bone Metastasis Management: A Narrative Review. Curr. Oncol., 33.","DOI":"10.3390\/curroncol33010065"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4103\/ijnpnd.ijnpnd_169_25","article-title":"Advances in AI and Machine Learning for Diagnostic Imaging of Bone and Soft Tissue Tumors: A Narrative Review","volume":"16","author":"Gupta","year":"2026","journal-title":"Int. J. Nutr. Pharmacol. Neurol. Dis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104875","DOI":"10.1016\/j.jbi.2025.104875","article-title":"Rapid review: Growing usage of Multimodal Large Language Models in healthcare","volume":"169","author":"Gupta","year":"2025","journal-title":"J. Biomed. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e70017","DOI":"10.1049\/htl2.70017","article-title":"Artificial Intelligence Challenges in the Healthcare Industry: A Systematic Review of Recent Evidence","volume":"12","author":"Mehraeen","year":"2025","journal-title":"Health Technol. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3348\/kjr.2023.0671","article-title":"Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study","volume":"25","author":"Kim","year":"2024","journal-title":"Korean J. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jin, J., Zhou, H., Sun, S., Tian, Z., Ren, H., Feng, J., and Jiang, X. (2023). Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1121594"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.compbiomed.2017.03.024","article-title":"A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks","volume":"84","author":"Wang","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112463","DOI":"10.1016\/j.ejrad.2025.112463","article-title":"Deep learning and transformer-based feature fusion of conventional MRI for differentiating spinal osteolytic bone metastases and multiple myeloma","volume":"194","author":"Bian","year":"2025","journal-title":"Eur. J. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10566","DOI":"10.21037\/qims-2025-922","article-title":"Automatic and observable spinal cord compression grading system for lung cancer spinal metastasis","volume":"15","author":"Wang","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1097\/RCT.0000000000001591","article-title":"Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis","volume":"48","author":"Hu","year":"2024","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.3390\/tomography10070075","article-title":"Fat Fraction Extracted from Whole-Body Magnetic Resonance (WB-MR) in Bone Metastatic Prostate Cancer: Intra- and Inter-Reader Agreement of Single-Slice and Volumetric Measurements","volume":"10","author":"Agazzi","year":"2024","journal-title":"Tomography"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1002\/jmri.28954","article-title":"Assessment of Hidden Blood Loss in Spinal Metastasis Surgery: A Comprehensive Approach with MRI-Based Radiomics Models","volume":"59","author":"Zhao","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Donners, R., Candito, A., Rata, M., Sharp, A., Messiou, C., Koh, D.-M., Tunariu, N., and Blackledge, M.D. (2024). Inter- and Intra-Patient Repeatability of Radiomic Features from Multiparametric Whole-Body MRI in Patients with Metastatic Prostate Cancer. Cancers, 16.","DOI":"10.3390\/cancers16091647"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100483","DOI":"10.1016\/j.jbo.2023.100483","article-title":"Deep learning-based magnetic resonance imaging of the spine in the diagnosis and physiological evaluation of spinal metastases","volume":"40","author":"Wang","year":"2023","journal-title":"J. Bone Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100498","DOI":"10.1016\/j.jbo.2023.100498","article-title":"Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases","volume":"42","author":"Wang","year":"2023","journal-title":"J. Bone Oncol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e823","DOI":"10.1016\/j.wneu.2023.04.029","article-title":"Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods","volume":"175","author":"Duan","year":"2023","journal-title":"World Neurosurg."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s40644-023-00585-4","article-title":"An MRI-based radiomics nomogram for differentiating spinal metastases from multiple myeloma","volume":"23","author":"Zhang","year":"2023","journal-title":"Cancer Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1097\/RCT.0000000000001465","article-title":"Radiomics of Spinal Metastases Originating From Primary Nonsmall Cell Lung Cancer or Breast Cancer and Ability to Predict Epidermal Growth Factor Receptor Mutation\/Ki-67 Levels","volume":"47","author":"Niu","year":"2023","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107811","DOI":"10.1016\/j.cmpb.2023.107811","article-title":"Computer-aided diagnosis of skeletal metastases in multi-parametric whole-body MRI","volume":"242","author":"Ceranka","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1007\/s11547-022-01468-7","article-title":"Diffusion-weighted MRI radiomics of spine bone tumors: Feature stability and machine learning-based classification performance","volume":"127","author":"Gitto","year":"2022","journal-title":"La Radiol. Medica"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1007\/s00330-021-08150-y","article-title":"Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: Influence of features number on logistic regression model performance","volume":"32","author":"Liu","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, K., Cao, J., Zhang, X., Wang, X., Zhao, X., Li, Q., Chen, S., Wang, P., Liu, T., and Du, J. (2022). Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.981769"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.mri.2022.05.012","article-title":"Radiomics analysis based on multiple parameters MR imaging in the spine: Predicting treatment response of osteolytic bone metastases to chemotherapy in breast cancer patients","volume":"92","author":"Shi","year":"2022","journal-title":"Magn. Reson. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xiong, X., Wang, J., Hu, S., Dai, Y., Zhang, Y., and Hu, C. (2021). Differentiating Between Multiple Myeloma and Metastasis Subtypes of Lumbar Vertebra Lesions Using Machine Learning\u2013Based Radiomics. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.601699"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1002\/jmri.27579","article-title":"MultiparametricMRI-Based Radiomics Approaches for Preoperative Prediction ofEGFRMutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma","volume":"54","author":"Jiang","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Haubold, J., Demircioglu, A., Theysohn, J.M., Wetter, A., Radbruch, A., D\u00f6rner, N., Schlosser, T.W., Deuschl, C., Li, Y., and Nassenstein, K. (2021). Generating Virtual Short Tau Inversion Recovery (STIR) Images from T1- and T2-Weighted Images Using a Conditional Generative Adversarial Network in Spine Imaging. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091542"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhong, X., Li, L., Jiang, H., Yin, J., Lu, B., Han, W., Li, J., and Zhang, J. (2020). Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization. BMC Med. Imaging, 20.","DOI":"10.1186\/s12880-020-00502-2"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s11547-018-0935-y","article-title":"Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: A feasibility study","volume":"124","author":"Filograna","year":"2018","journal-title":"La Radiol. Medica"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.compbiomed.2016.04.006","article-title":"Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images","volume":"73","author":"Rangayyan","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e540","DOI":"10.1016\/j.wneu.2023.01.076","article-title":"Application of Radiomics to the Differential Diagnosis of Temporal Bone Skull Base Lesions: A Pilot Study","volume":"172","author":"Findlay","year":"2023","journal-title":"World Neurosurg."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"225021","DOI":"10.1088\/1361-6560\/ae19c5","article-title":"AI-driven software for automated quantification of skeletal metastases and treatment response evaluation using whole-body diffusion-weighted MRI (WB-DWI) in advanced prostate cancer","volume":"70","author":"Candito","year":"2025","journal-title":"Phys. Med. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5","DOI":"10.17691\/stm2024.16.3.01","article-title":"Radiomics and Quantitative MDA Criteria in Breast Cancer with Bone Metastases by MRI: Examples of Calculation Algorithms and Their Practical Use","volume":"16","author":"Steinhauer","year":"2024","journal-title":"Sovrem. Tehnol. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fan, Y., Dong, Y., Sun, X., Wang, H., Zhao, P., Wang, H., and Jiang, X. (2022). Development and validation of MRI-based radiomics signatures as new markers for preoperative assessment of EGFR mutation and subtypes from bone metastases. BMC Cancer, 22.","DOI":"10.1186\/s12885-022-09985-4"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"16","DOI":"10.17691\/stm2022.14.2.02","article-title":"Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion","volume":"14","author":"Steinhauer","year":"2022","journal-title":"Sovrem. Tehnol. Med."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5294379","DOI":"10.1155\/2021\/5294379","article-title":"Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis","volume":"2021","author":"Fan","year":"2021","journal-title":"Contrast Media Mol. Imaging"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.1016\/j.acra.2021.06.004","article-title":"MRI-Based Radiomics Nomogram as a Potential Biomarker to Predict the EGFR Mutations in Exon 19 and 21 Based on Thoracic Spinal Metastases in Lung Adenocarcinoma","volume":"29","author":"Cao","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liu, X., Han, C., Cui, Y., Xie, T., Zhang, X., and Wang, X. (2021). Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients with Prostate Cancer Based on Deep Learning. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.773299"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Colombo, A., Saia, G., Azzena, A.A., Rossi, A., Zugni, F., Pricolo, P., Summers, P.E., Marvaso, G., Grimm, R., and Bellomi, M. (2021). Semi-Automated Segmentation of Bone Metastases from Whole-Body MRI: Reproducibility of Apparent Diffusion Coefficient Measurements. Diagnostics, 11.","DOI":"10.3390\/diagnostics11030499"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Blackledge, M.D., Collins, D.J., Tunariu, N., Orton, M.R., Padhani, A.R., Leach, M.O., and Koh, D.-M. (2014). Assessment of Treatment Response by Total Tumor Volume and Global Apparent Diffusion Coefficient Using Diffusion-Weighted MRI in Patients with Metastatic Bone Disease: A Feasibility Study. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0091779"},{"key":"ref_59","first-page":"100627","article-title":"Spinal magnetic resonance image segmentation based on U-net","volume":"16","author":"Wang","year":"2023","journal-title":"J. Radiat. Res. Appl. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e232085","DOI":"10.1148\/radiol.232085","article-title":"Weakly Supervised Deep Learning in Radiology","volume":"312","author":"Misera","year":"2024","journal-title":"Radiology"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"13724","DOI":"10.1038\/s41598-020-69920-0","article-title":"Variability and reproducibility in deep learning for medical image segmentation","volume":"10","author":"Renard","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e242961","DOI":"10.1148\/radiol.242961","article-title":"Generative AI and Foundation Models in Radiology: Applications, Opportunities, and Potential Challenges","volume":"317","author":"Tavakoli","year":"2025","journal-title":"Radiology"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hallinan, J.T.P.D., Zhu, L., Zhang, W., Lim, D.S.W., Baskar, S., Low, X.Z., Yeong, K.Y., Teo, E.C., Kumarakulasinghe, N.B., and Yap, Q.V. (2022). Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.849447"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.critrevonc.2005.04.005","article-title":"Epidural spinal cord compression","volume":"56","author":"Caraceni","year":"2005","journal-title":"Crit. Rev. Oncol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"6243s","DOI":"10.1158\/1078-0432.CCR-06-0931","article-title":"Clinical Features of Metastatic Bone Disease and Risk of Skeletal Morbidity","volume":"12","author":"Coleman","year":"2006","journal-title":"Clin. Cancer Res."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Varnosfaderani, S.M., and Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11.","DOI":"10.3390\/bioengineering11040337"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4444","DOI":"10.1038\/s41467-025-59433-7","article-title":"A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans","volume":"16","author":"Zhang","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1227\/neu.0000000000003683","article-title":"Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models","volume":"98","author":"Sangwon","year":"2025","journal-title":"Neurosurgery"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"20503121221097582","DOI":"10.1177\/20503121221097582","article-title":"Effective examination methods for identifying the primary origins of metastatic bone tumors of unknown primary origin during the initial visit: A retrospective chart review study","volume":"10","author":"Fujibuchi","year":"2022","journal-title":"SAGE Open Med."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.diii.2021.05.009","article-title":"Comparison of conventional magnetic resonance imaging and diffusion-weighted imaging in the differentiation of bone plasmacytoma from bone metastasis in the extremities","volume":"102","author":"Hwang","year":"2021","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sanker, V., Dawer, P., Thaller, A., Li, Z., Heesen, P., Hariharan, S., Nordin, E.O.R., Cavagnaro, M.J., Ratliff, J., and Desai, A. (2025). Artificial Intelligence Models for Predicting Outcomes in Spinal Metastasis: A Systematic Review and Meta-Analysis. J. Clin. Med., 14.","DOI":"10.3390\/jcm14165885"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e36196","DOI":"10.1097\/MD.0000000000036198","article-title":"Research status and progress of radiomics in bone and soft tissue tumors: A review","volume":"102","author":"Zhang","year":"2023","journal-title":"Medicine"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"144","DOI":"10.14245\/ns.2448846.423","article-title":"Predicting Postoperative Progression of Ossification of the Posterior Longitudinal Ligament in the Cervical Spine Using Interpretable Radiomics Models","volume":"22","author":"Qin","year":"2025","journal-title":"Neurospine"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1148\/radiol.2016142084","article-title":"Whole-Body MR Imaging: Musculoskeletal Applications","volume":"279","author":"Lecouvet","year":"2016","journal-title":"Radiology"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1097\/RLI.0000000000000996","article-title":"Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients with Lung or Colon Cancer","volume":"58","author":"Rockall","year":"2023","journal-title":"Investig. Radiol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1007\/s00256-024-04723-2","article-title":"Present and future of whole-body MRI in metastatic disease and myeloma: How and why you will do it","volume":"53","author":"Lecouvet","year":"2024","journal-title":"Skelet. Radiol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.jacr.2020.06.001","article-title":"National Trends in Oncologic Diagnostic Imaging","volume":"17","author":"Rosenkrantz","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Bendella, Z., Widmann, C.N., Kindler, C., Haase, R., Sauer, M., Heneka, M.T., Radbruch, A., and Schmeel, F.C. (2025). Longitudinal Monitoring of Brain Volume Changes After COVID-19 Infection Using Artificial Intelligence-Based MRI Volumetry. Diagnostics, 15.","DOI":"10.3390\/diagnostics15243244"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Najjar, R. (2023). Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics, 13.","DOI":"10.20944\/preprints202306.1124.v1"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Obuchowicz, R., Lasek, J., Wodzi\u0144ski, M., Pi\u00f3rkowski, A., Strzelecki, M., and Nurzynska, K. (2025). Artificial Intelligence-Empowered Radiology\u2014Current Status and Critical Review. Diagnostics, 15.","DOI":"10.3390\/diagnostics15030282"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"100188","DOI":"10.1016\/j.mcpdig.2024.100188","article-title":"Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations","volume":"3","author":"Korfiatis","year":"2024","journal-title":"Mayo Clin. Proc. Digit. Health"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1007\/s00330-023-10506-5","article-title":"Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: A multi-center study","volume":"34","author":"Ye","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Pu, F., Hu, Z., Yang, Y., Xia, P., and Xia, Z. (2023). Editorial: Diagnosis and treatment of bone metastases. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1247231"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3892\/mi.2026.297","article-title":"Metastatic cancer detection and management with artificial intelligence and augmented reality (Review)","volume":"6","author":"Kukunoor","year":"2026","journal-title":"Med. Int."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1002\/ird3.70027","article-title":"AI-Enhanced Predictive Imaging in Precision Medicine: Advancing Diagnostic Accuracy and Personalized Treatment","volume":"3","author":"Rajendran","year":"2025","journal-title":"iRADIOLOGY"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1038\/s41746-025-02226-5","article-title":"Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses","volume":"9","author":"McLeod","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1186\/s40001-025-03003-6","article-title":"Emerging biomarkers for early cancer detection and diagnosis: Challenges, innovations, and clinical perspectives","volume":"30","author":"Zafar","year":"2025","journal-title":"Eur. J. Med. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.trecan.2024.12.001","article-title":"Artificial intelligence-based biomarkers for treatment decisions in oncology","volume":"11","author":"Ligero","year":"2025","journal-title":"Trends Cancer"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Li, T., Huang, H., Zhang, S., Zhang, Y., Jing, H., Sun, T., Zhang, X., Lu, L., and Zhang, M. (2022). Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.984750"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Elshimy, Y., Alkhatib, A.R., Atassi, B., and Mohammad, K.S. (2025). Biomarker-Driven Approaches to Bone Metastases: From Molecular Mechanisms to Clinical Applications. Biomedicines, 13.","DOI":"10.3390\/biomedicines13051160"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"100073","DOI":"10.1016\/j.ejrai.2026.100073","article-title":"Artificial intelligence in radiology workflow: A systematic review into protocol automation and clinical applications","volume":"5","author":"Hadi","year":"2026","journal-title":"Eur. J. Radiol. Artif. Intell."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Nair, A., Ong, W., Lee, A., Leow, N.W., Makmur, A., Ting, Y.H., Lee, Y.J., Ong, S.J., Tan, J.J.H., and Kumar, N. (2025). Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review. Diagnostics, 15.","DOI":"10.3390\/diagnostics15091146"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Faiella, E., Santucci, D., Calabrese, A., Russo, F., Vadal\u00e0, G., Zobel, B.B., Soda, P., Iannello, G., de Felice, C., and Denaro, V. (2022). Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19031880"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Sanker, V., Gowda, P., Thaller, A., Li, Z., Heesen, P., Qiang, Z., Hariharan, S., Nordin, E.O.R., Cavagnaro, M.J., and Ratliff, J. (2025). Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review. J. Clin. Med., 14.","DOI":"10.3390\/jcm14165877"},{"key":"ref_95","first-page":"e79199","article-title":"Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review","volume":"17","author":"Debellotte","year":"2025","journal-title":"Cureus"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"8266","DOI":"10.1007\/s00330-022-08945-7","article-title":"Validating the screening criteria for bone metastases in treatment-na\u00efve unfavorable intermediate and high-risk prostate cancer\u2014The prevalence and location of bone- and lymph node metastases","volume":"32","author":"Rud","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_97","first-page":"2443","article-title":"The Role of Artificial Intelligence in Improving Diagnostic Accuracy in Medical Imaging: A Review","volume":"85","author":"Sabri","year":"2025","journal-title":"Comput. Mater. Contin."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Zhou, W., Ye, Z., Huang, G., Zhang, X., Xu, M., Liu, B., Zhuang, B., Tang, Z., Wang, S., and Chen, D. (2024). Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images. BMC Med., 22.","DOI":"10.1186\/s12916-024-03247-9"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s12553-025-00970-y","article-title":"The role of AI in mitigating the impact of radiologist shortages: A systematised review","volume":"15","author":"Achour","year":"2025","journal-title":"Health Technol."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"102506","DOI":"10.1016\/j.artmed.2023.102506","article-title":"Rams, hounds and white boxes: Investigating human\u2013AI collaboration protocols in medical diagnosis","volume":"138","author":"Cabitza","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/s44401-025-00023-6","article-title":"AI solutions to the radiology workforce shortage","volume":"2","author":"Jing","year":"2025","journal-title":"npj Health Syst."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"779","DOI":"10.3171\/2020.8.SPINE20963","article-title":"Utility of prediction model score: A proposed tool to standardize the performance and generalizability of clinical predictive models based on systematic review","volume":"34","author":"Ehresman","year":"2021","journal-title":"J. Neurosurg. Spine"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, Y., Wang, F., Zhang, Y., and Huang, D. (2025). Addressing the current challenges in the clinical application of AI-based Radiomics for cancer imaging. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1674397"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"00368504211029777","DOI":"10.1177\/00368504211029777","article-title":"Machine learning on small size samples: A synthetic knowledge synthesis","volume":"105","author":"Kokol","year":"2022","journal-title":"Sci. Prog."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"8803","DOI":"10.1097\/MS9.0000000000004166","article-title":"Assessing the generalizability of artificial intelligence in radiology: A systematic review of performance across different clinical settings","volume":"87","author":"Suleman","year":"2025","journal-title":"Ann. Med. Surg."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2686","DOI":"10.1038\/s41591-023-02540-z","article-title":"External validation of AI models in health should be replaced with recurring local validation","volume":"29","author":"Youssef","year":"2023","journal-title":"Nat. Med."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"5414","DOI":"10.1007\/s00330-025-12307-4","article-title":"ESR Essentials: Bone marrow MRI in oncology\u2014Practice recommendations by the European Society of Musculoskeletal Radiology","volume":"36","author":"Lecouvet","year":"2026","journal-title":"Eur. Radiol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1097\/RLI.0000000000000666","article-title":"Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence","volume":"55","author":"Hagiwara","year":"2020","journal-title":"Investig. Radiol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1016\/j.acra.2012.02.002","article-title":"Research Imaging in an Academic Medical Center","volume":"19","author":"Armato","year":"2012","journal-title":"Acad. Radiol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"e232653","DOI":"10.1148\/radiol.232653","article-title":"Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles","volume":"311","author":"Tejani","year":"2024","journal-title":"Radiology"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"e26297","DOI":"10.1016\/j.heliyon.2024.e26297","article-title":"Ethical and regulatory challenges of AI technologies in healthcare: A narrative review","volume":"10","author":"Mennella","year":"2024","journal-title":"Heliyon"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1186\/s13244-025-02120-4","article-title":"Implementation of AI in radiology: The perspective of referring physicians","volume":"16","author":"Gotta","year":"2025","journal-title":"Insights Imaging"},{"key":"ref_113","first-page":"75","article-title":"Bias in artificial intelligence for medical imaging: Fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects","volume":"31","author":"Ponsiglione","year":"2024","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"ref_115","first-page":"e101185","article-title":"Strategies for Embedding Prediction Models in Clinical Decision-Making Workflows","volume":"18","author":"Amusa","year":"2026","journal-title":"Cureus"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s12031-025-02350-7","article-title":"The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment","volume":"75","author":"Mashhour","year":"2025","journal-title":"J. Mol. Neurosci."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"199","DOI":"10.4097\/kja.25075","article-title":"Comprehensive reporting guidelines and checklist for studies developing and utilizing artificial intelligence models","volume":"78","author":"Kwak","year":"2025","journal-title":"Korean J. Anesthesiol."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"e15734056370620","DOI":"10.2174\/0115734056370620250403030638","article-title":"Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow Efficiency","volume":"21","year":"2025","journal-title":"Curr. Med. Imaging Former. Curr. Med. Imaging Rev."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"a031252","DOI":"10.1101\/cshperspect.a031252","article-title":"The Biology of Bone Metastasis","volume":"8","author":"Esposito","year":"2017","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Parillo, M., and Mallio, C.A. (2025). The Whole-Body MRI Reporting and Data System Guidelines for Prostate Cancer (MET-RADS-P), Multiple Myeloma (MY-RADS), and Cancer Screening (ONCO-RADS). Cancers, 17.","DOI":"10.3390\/cancers17020275"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","article-title":"The Medical Segmentation Decathlon","volume":"13","author":"Antonelli","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"20200975","DOI":"10.1259\/bjr.20200975","article-title":"Artificial intelligence in paediatric radiology: Future opportunities","volume":"94","author":"Davendralingam","year":"2020","journal-title":"Br. J. Radiol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"103228","DOI":"10.1016\/j.eclinm.2025.103228","article-title":"Artificial intelligence for diagnostics in radiology practice: A rapid systematic scoping review","volume":"83","author":"Lawrence","year":"2025","journal-title":"eClinicalMedicine"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"115388","DOI":"10.4329\/wjr.v17.i12.115388","article-title":"Radiologists\u2019 perspectives on the use of artificial intelligence in emergency radiology: A pilot survey","volume":"17","author":"Centini","year":"2025","journal-title":"World J. Radiol."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s44387-025-00042-6","article-title":"MAIA: A collaborative medical AI platform for integrated healthcare innovation","volume":"1","author":"Bendazzoli","year":"2025","journal-title":"npj Artif. Intell."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/17\/2794\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:34:44Z","timestamp":1787920484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/17\/2794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,28]]},"references-count":125,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["cancers18172794"],"URL":"https:\/\/doi.org\/10.3390\/cancers18172794","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,28]]}}}