Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229–263 (2024).
Google Scholar
Coleman, R. E. et al. Bone metastases. Nat. Rev. Dis. Prim. 6, 83 (2020).
Google Scholar
Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).
Google Scholar
Lor Randall, R. (ed) Metastatic Bone Disease: An Integrated Approach to Patient Care (Springer Nature, 2024).
Coleman, R. E. Clinical features of metastatic bone disease and risk of skeletal morbidity. Clin. Cancer Res. 12, 6243s–6249s (2006).
Google Scholar
Coleman, R. E., Brown, J. & Holen, I. in Abeloff’s Clinical Oncology 6th edn (eds Niederhuber, J. E. et al.) 809–830.e3 (Elsevier, 2020).
Zhang, J., Cai, D. & Hong, S. Prevalence and prognosis of bone metastases in common solid cancers at initial diagnosis: a population-based study. BMJ Open 13, e069908 (2023).
Google Scholar
Hernandez, R. K. et al. Incidence of bone metastases in patients with solid tumors: analysis of oncology electronic medical records in the United States. BMC Cancer 18, 44 (2018).
Google Scholar
Coleman, R. et al. Bone health in cancer: ESMO Clinical Practice Guidelines. Ann. Oncol. 31, 1650–1663 (2020).
Google Scholar
Coleman, R. et al. Bone health in cancer patients: ESMO Clinical Practice Guidelines. Ann. Oncol. 25, iii124–iii137 (2014).
Google Scholar
Jensen, A. Ø et al. Incidence of bone metastases and skeletal-related events in breast cancer patients: a population-based cohort study in Denmark. BMC Cancer 11, 29 (2011).
Google Scholar
Nørgaard, M. et al. Skeletal related events, bone metastasis and survival of prostate cancer: a population based cohort study in Denmark (1999 to 2007). J. Urol. 184, 162–167 (2010).
Google Scholar
Cetin, K., Christiansen, C. F., Jacobsen, J. B., Nørgaard, M. & Sørensen, H. T. Bone metastasis, skeletal-related events, and mortality in lung cancer patients: a Danish population-based cohort study. Lung Cancer 86, 247–254 (2014).
Google Scholar
Boire, A. et al. Why do patients with cancer die?. Nat. Rev. Cancer 24, 578–589 (2024).
Google Scholar
Oster, G. et al. Natural history of skeletal-related events in patients with breast, lung, or prostate cancer and metastases to bone: a 15-year study in two large US health systems. Support Care Cancer 21, 3279–3286 (2013).
Google Scholar
von Moos, R. et al. Management of bone health in solid tumours: from bisphosphonates to a monoclonal antibody. Cancer Treat. Rev. 76, 57–67 (2019).
Google Scholar
Hong, J. H. et al. Development and validation of a radiomics model for differentiating bone islands and osteoblastic bone metastases at abdominal CT. Radiology 299, 626–632 (2021).
Google Scholar
Schulman, K. L. & Kohles, J. Economic burden of metastatic bone disease in the US. Cancer 109, 2334–2342 (2007).
Google Scholar
DiCaprio, M. R., Murtaza, H., Palmer, B. & Evangelist, M. Narrative review of the epidemiology, economic burden, and societal impact of metastatic bone disease. Ann. Jt 7, 28 (2022).
Google Scholar
Dong, X. et al. Artificial intelligence in skeletal metastasis imaging. Comput. Struct. Biotechnol. J. 23, 157–164 (2024).
Google Scholar
Koike, Y. et al. Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans. Int. J. Comput. Assist. Radio. Surg. 18, 1867–1874 (2023).
Google Scholar
Hammon, M. et al. Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography. Eur. Radio. 23, 1862–1870 (2013).
Google Scholar
Burns, J. E. et al. Automated detection of sclerotic metastases in the thoracolumbar spine at CT. Radiology 268, 69–78 (2013).
Google Scholar
Sun, W. et al. A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours. Cancer Imaging 21, 20 (2021).
Google Scholar
Caloro, E. et al. Artificial intelligence in bone metastasis imaging: recent progresses from diagnosis to treatment—a narrative review. Crit. Rev. Oncog. 29, 77–90 (2024).
Google Scholar
Lacroix, M. et al. Artificial intelligence in musculoskeletal oncology imaging: a critical review of current applications. Diagn. Inter. Imaging 104, 18–23 (2023).
Google Scholar
Coleman, R. E., Fogelman, I., Habibollahi, F., North, W. R. & Rubens, R. D. Selection of patients with breast cancer for routine follow-up bone scans. Clin. Oncol. 2, 328–332 (1990).
Google Scholar
Shen, T.-X. et al. CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma. Cancer Imaging 19, 34 (2019).
Google Scholar
Yao, G. et al. Value of combining PET/CT and clinicopathological features in predicting EGFR mutation in lung adenocarcinoma with bone metastasis. J. Cancer 11, 5511–5517 (2020).
Google Scholar
Seeman, E. Bone quality: the material and structural basis of bone strength. J. Bone Min. Metab. 26, 1–8 (2008).
Google Scholar
Hendriks, L. E. L. et al. Non-small-cell lung cancer. Nat. Rev. Dis. Prim. 10, 71 (2024).
Google Scholar
Kim, C. & Giaccone, G. Precision oncology in non-small-cell lung cancer: opportunities and challenges. Nat. Rev. Clin. Oncol. 15, 348–349 (2018).
Google Scholar
Lloyd, M. R., Jhaveri, K., Kalinsky, K., Bardia, A. & Wander, S. A. Precision therapeutics and emerging strategies for HR-positive metastatic breast cancer. Nat. Rev. Clin. Oncol. 21, 743–761 (2024).
Google Scholar
Yin, J. J., Pollock, C. B. & Kelly, K. Mechanisms of cancer metastasis to the bone. Cell Res. 15, 57–62 (2005).
Google Scholar
Alexander, R. et al. Mandating limits on workload, duty, and speed in radiology. Radiology 304, 274–282 (2022).
Google Scholar
Taylor-Phillips, S. & Stinton, C. Fatigue in radiology: a fertile area for future research. Br. J. Radiol. 92, 20190043 (2019).
Google Scholar
Li, M. D. et al. Artificial intelligence applied to musculoskeletal oncology: a systematic review. Skelet. Radio. 51, 245–256 (2022).
Google Scholar
Deng, Z. et al. (eds) Foundation Models for General Medical AI: Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings (Springer Nature, 2025).
Oquab, M. et al. DINOv2: learning robust visual features without supervision. Trans. Mach. Learn. Artif. Intell. (2024).
Bi, W. L. et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J. Clin. 69, 127–157 (2019).
Google Scholar
Liu, X. et al. A generalist medical language model for disease diagnosis assistance. Nat. Med. 31, 932–942 (2025).
Google Scholar
Ankush, A. VoxRad: building an open-source locally-hosted radiology reporting system. Clin. Imaging 119, 110414 (2025).
Google Scholar
Xu, H. et al. A whole-slide foundation model for digital pathology from real-world data. Nature 630, 181–188 (2024).
Google Scholar
Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).
Google Scholar
Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual-language foundation model for pathology image analysis using medical Twitter. Nat. Med. 29, 2307–2316 (2023).
Google Scholar
Zhang, S. & Metaxas, D. On the challenges and perspectives of foundation models for medical image analysis. Med. Image Anal. 91, 102996 (2024).
Google Scholar
Zhou, H. Y., Acosta, J. N., Adithan, S. & Datta, S. MedVersa: a generalist foundation model for diverse medical imaging tasks. N. Engl. J. Med. AI (2026).
Nath, V. et al. VILA-M3: enhancing vision-language models with medical expert knowledge. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. 14788–14798 (IEEE, 2025).
von Schacky, C. E. et al. Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology 301, 398–406 (2021).
Google Scholar
Wang, H. et al. Deep learning models in classifying primary bone tumors and bone infections based on radiographs. NPJ Precis. Oncol. 9, 72 (2025).
Google Scholar
He, Y. et al. Deep learning-based classification of primary bone tumors on radiographs: a preliminary study. EBioMedicine 62, 103121 (2020).
Google Scholar
Yin, S. et al. A survey on multimodal large language models. Natl Sci. Rev. 11, nwae403 (2024).
Google Scholar
Kamath, U., Keenan, K., Somers, G. & Sorenson, S. Large Language Models: A Deep Dive: Bridging Theory and Practice (Springer Nature, 2024).
Wei, J. et al. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems 35 (eds Koyejo, S. et al.) 24824–24837 (Curran Associates, 2022).
Blankemeier, L. et al. Merlin: a computed tomography vision-language foundation model and dataset. Nature 652, 1318–1328 (2026).
Greenbaum, S. L., Thornhill, B. A. & Geller, D. S. Characterization and surgical management of metastatic disease of the tibia. Am. J. Orthop. 46, E423 (2017).
Google Scholar
Kelly, C. M., Wilkins, R. M., Eckardt, J. J. & Ward, W. G. Treatment of metastatic disease of the tibia. Clin. Orthop. Relat. Res. 415 (Suppl.), S219–S229 (2003).
Google Scholar
Li, Z. et al. Fibrinogen-albumin ratio index exhibits predictive value of neoadjuvant chemotherapy in osteosarcoma. Cancer Manag. Res. 14, 1671–1682 (2022).
Google Scholar
Zhou, L. et al. Preoperative CT for prediction of local recurrence after curettage of giant cell tumor of bone. J. Bone Oncol. 29, 100366 (2021).
Google Scholar
Zhao, Q. et al. Chondroblastoma: clinicopathological analyses of 307 cases from a single institution in China and the diagnostic value of the H3F3 K36M mutant antibody. J. Clin. Pathol. 76, 367–373 (2023).
Google Scholar
Luo, Y. et al. Diagnostic value of H3F3A mutation and clinicopathological features of giant cell tumours in non-long bones. J. Bone Oncol. 38, 100467 (2023).
Google Scholar
Schajowicz, F. & McGuire, M. H. Diagnostic difficulties in skeletal pathology. Clin. Orthop. Relat. Res. 240, 281–310 (1989).
Google Scholar
Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K. & Gurevych, I. AdapterFusion: non-destructive task composition for transfer learning. In Proc. 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (eds Merlo, P. et al.) 503 – 487 (Association for Computational Linguistics, 2021).
Raisi, E. & Bach, S. H. Selecting auxiliary data using knowledge graphs for image classification with limited labels. In Proc 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 4026–4031 (IEEE, 2020); https://doi.org/10.1109/cvprw50498.2020.00473
Grand View Research. PET Scanners Market Size, Share & Trends Analysis Report, 2030 (Grand View Research, Inc., 2024); https://www.grandviewresearch.com/industry-analysis/pet-scanners-market-report
GlobalData. Diagnostic Imaging (DI) Market size, Share, Trends and Analysis by Product Type, Region and Segment Forecast to 2033 (GlobalData, 2023); https://www.globaldata.com/store/report/diagnostic-imaging-market-analysis/
Thakur, V. & Dey, K. Bone Scan Market Research Report Information: By Product (Radiopharmaceuticals, Imaging Devices), Application (Fractures, Arthritis, Paget’s Disease of Bone, Cancer Originating in Bone), End User (Hospitals, Clinics, Diagnostic Centers)—Global Forecast till 2035 (Market Research Future, 2026); https://www.marketresearchfuture.com/reports/bone-scan-market-5027
De Chiffre, L., Carmignato, S., Kruth, J.-P., Schmitt, R. & Weckenmann, A. Industrial applications of computed tomography. CIRP Annals 63, 655–677 (2014).
Google Scholar
Ogbole, G. I., Adeyomoye, A. O., Badu-Peprah, A., Mensah, Y. & Nzeh, D. A. Survey of magnetic resonance imaging availability in West Africa. Pan Afr. Med J. 30, 240 (2018).
Google Scholar
Kritskiy, A. in Magnetic Materials and Technologies for Medical Applications (ed Tishin, A. M.) 613–623 (Elsevier, 2022).
Guo, X. et al. Synchronous bone metastasis in lung cancer: retrospective study of a single center of 15,716 patients from Tianjin, China. BMC Cancer 21, 613 (2021).
Google Scholar
Hong, S., Youk, T., Lee, S. J., Kim, K. M. & Vajdic, C. M. Bone metastasis and skeletal-related events in patients with solid cancer: a Korean nationwide health insurance database study. PLoS ONE 15, e0234927 (2020).
Google Scholar
Lower, E. E., Khan, S., Kennedy, D. & Baughman, R. P. Discordance of the estrogen receptor and HER-2/neu in breast cancer from primary lesion to first and second metastatic site. Breast Cancer 9, 515–520 (2017).
Google Scholar
Skoulidis, F. & Heymach, J. V. Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy. Nat. Rev. Cancer 19, 495–509 (2019).
Google Scholar
Dosovitskiy, A. et al. An image is worth 16 × 16 words: transformers for image recognition at scale. In Proc. International Conference on Learning Representations 45–67 (2021).
Darcet, T., Oquab, M., Mairal, J. & Bojanowski, P. Vision transformers need registers. In Proc. the Twelfth International Conference on Learning Representations (2024).



