{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T06:19:50Z","timestamp":1787552390296,"version":"build-2736575974"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>\n                    Bone metastases are a major complication of advanced solid tumors and are associated with substantial morbidity and reduced survival. Imaging plays a central role in detection and monitoring, yet conventional modalities and qualitative risk scores remain limited by suboptimal specificity and inter-reader variability. Artificial intelligence (AI) has emerged as a potential tool to improve diagnostic accuracy and prognostic assessment. We performed a systematic review and meta-analysis of studies published between January 2008 and January 2026 evaluating AI-based models for the diagnosis and\/or prognosis of bone metastases. PubMed\/MEDLINE, Scopus, and Web of Science were searched. Studies reporting quantitative performance metrics were included. Logit-transformed area under the curve (AUC) values were pooled using a random-effects model with restricted maximum likelihood estimation. Twenty-two studies met the eligibility criteria, encompassing highly heterogeneous datasets ranging from small single-center cohorts to large population-based registries and multicenter imaging databases. Most were retrospective and single-center. The overall AUC was 0.911 (95% CI 0.868\u20130.940), with substantial heterogeneity (I\u00b2 = 98.8%) Radiomics-based models showed consistently high performance with minimal heterogeneity, after group stratification, whereas clinical-only models demonstrated lower discriminative ability. Prognostic studies were heterogeneous and were synthesized narratively. AI-based models demonstrate high diagnostic performance for bone metastases across imaging modalities. However, methodological variability and limited external validation currently restrict clinical translation, underscoring the need for prospective multicenter studies. Most included studies were retrospective, single-center investigations with limited external validation. Therefore, despite the encouraging diagnostic performance, prospective multicenter studies and standardized reporting remain necessary before routine clinical implementation. Unlike previous systematic reviews, this study provides a quantitative synthesis of diagnostic performance together with a structured appraisal of methodological quality and clinical translational readiness. This systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 statement and prospectively registered (CRD420261350260).\n                    <jats:bold>Systematic review registration:<\/jats:bold>\n                    <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD420261350260<\/jats:ext-link>\n                    , identifier CRD420261350260.\n                  <\/jats:p>","DOI":"10.3389\/fonc.2026.1915919","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T05:33:01Z","timestamp":1787549581000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for bone metastases: a systematic review and meta-analysis of diagnostic and prognostic performance"],"prefix":"10.3389","volume":"16","author":[{"given":"Luisana","family":"Sisca","sequence":"first","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]},{"name":"Department of Translational Research, Institut Curie","place":["Paris, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariam Grazia","family":"Polito","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]},{"name":"Community Oncology Unit, Azienda Sanitaria Locale (ASL) Latina, Casa della Salute (CDS) Aprilia, Sapienza University of Rome","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emy","family":"Sisca","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria","place":["Rende, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe Francesco","family":"Papalia","sequence":"additional","affiliation":[{"name":"Oncological Orthopedics Department, Istituti Fisioterapici Ospitalieri (IFO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Vilardi","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Venuti","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Cortellini","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]},{"name":"Department of Surgery and Cancer, Imperial College London","place":["London, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianna","family":"Silletta","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberta","family":"Scafetta","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Calagna","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Iuliani","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Cavaliere","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonia","family":"Simonetti","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno","family":"Vincenzi","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Tonini","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Pantano","sequence":"additional","affiliation":[{"name":"Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico","place":["Rome, Italy"]},{"name":"Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma","place":["Rome, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbo.2025.100738","article-title":"Real-life use of bone-targeting agents for bone metastases in France between 2009 and 2018: Results of the OPTIMOS study","volume":"56","author":"Confavreux","year":"2026","journal-title":"J Bone Oncol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.62347\/QFHJ2430","article-title":"Targeting cancer-induced skeletal damage: a holistic approach to understanding pathophysiology, mechanisms, and management solutions","volume":"15","author":"Gu","year":"2025","journal-title":"Am J Cancer Res"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1320574","article-title":"Nuclear medicine imaging for bone metastases assessment: what else besides bone scintigraphy in the era of personalized medicine","volume":"10","author":"Ouvrard","year":"2024","journal-title":"Front Med (Lausanne)"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3109\/10428199309148551","article-title":"Magnetic resonance imaging in patients with bone marrow disorders","volume":"10","author":"Negendank","year":"1993","journal-title":"Leuk Lymphoma"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1002\/jor.24660","article-title":"Fracture risk assessment and clinical decision making for patients with metastatic bone disease","volume":"38","author":"Damron","year":"2020","journal-title":"J Orthop Res"},{"key":"B6","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s40779-023-00458-8","article-title":"Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling","volume":"10","author":"Zhang","year":"2023","journal-title":"Mil Med Res"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbo.2025.100682","article-title":"Artificial intelligence in bone metastases: a systematic review in guideline adherence of 92 studies","volume":"52","author":"van der Linden","year":"2025","journal-title":"J Bone Oncol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1186\/s12885-025-13631-0","article-title":"Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis","volume":"25","author":"Tao","year":"2025","journal-title":"BMC Cancer"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1007\/s12672-025-03307-3","article-title":"Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach","volume":"16","author":"Huang","year":"2025","journal-title":"Discover Oncol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10916-008-9168-2","article-title":"Artificial neural network to predict skeletal metastasis in patients with prostate cancer","volume":"33","author":"Chiu","year":"2009","journal-title":"J Med Syst"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare8040493","article-title":"A lightweight convolutional neural network architecture applied for bone metastasis classification in nuclear medicine: a case study on prostate cancer patients","volume":"8","author":"Ntakolia","year":"2020","journal-title":"Healthcare (Basel)"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.1015952","article-title":"Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning","volume":"10","author":"Ji","year":"2022","journal-title":"Front Public Health"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021203783","article-title":"Development and validation of a radiomics model for differentiating bone islands and osteoblastic bone metastases at abdominal CT","volume":"299","author":"Hong","year":"2021","journal-title":"Radiology"},{"key":"B14","doi-asserted-by":"publisher","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":"B15","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s00432-023-05574-5","article-title":"Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer","volume":"150","author":"Zhang","year":"2024","journal-title":"J Cancer Res Clin Oncol"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2024.102617","article-title":"Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism","volume":"72","author":"Teng","year":"2024","journal-title":"EClinicalMedicine"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.21037\/gs-2025-168","article-title":"SEER-based machine learning prediction of bone metastasis in breast cancer: model development and validation","volume":"14","author":"Gao","year":"2025","journal-title":"Gland Surg"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbo.2025.100681","article-title":"Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model","volume":"52","author":"Li","year":"2025","journal-title":"J Bone Oncol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.2147\/IJGM.S465031","article-title":"Development and validation of a clinic machine learning classifier for the prediction of risk stratifications of prostate cancer bone metastasis progression to castration resistance","volume":"17","author":"Li","year":"2024","journal-title":"Int J Gen Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.mri.2023.12.009","article-title":"A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients","volume":"107","author":"Xinyang","year":"2024","journal-title":"Magn Reson Imaging"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.124.267751","article-title":"Composite prediction score to interpret bone focal uptake in hormone-sensitive prostate cancer patients imaged with [18F]PSMA-1007 PET\/CT","volume":"65","author":"Bauckneht","year":"2024","journal-title":"J Nucl Med"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-025-07091-8","article-title":"Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments","volume":"52","author":"Haberl","year":"2025","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-024-01242-3","article-title":"The fine-tuned large language model for extracting the progressive bone metastasis from unstructured radiology reports","volume":"38","author":"Kanemaru","year":"2025","journal-title":"J Imaging Inform Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.urology.2024.05.027","article-title":"Development and validation of a machine learning model for bone metastasis in prostate cancer: based on inflammatory and nutritional indicators","volume":"190","author":"Jin","year":"2024","journal-title":"Urology"},{"key":"B25","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1186\/s12905-024-03264-z","article-title":"Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Womens Health"},{"key":"B26","unstructured":"Development and Validation of an Early Diagnosis Model for Bone Metastasis in non-Small Cell Lung Cancer Based on Serological Characteristics of the Bone Metastasis Mechanism - Eclinicalmedicine"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.1095059","article-title":"Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: an internally and externally validated study using machine-learning techniques","volume":"12","author":"Cui","year":"2022","journal-title":"Front Oncol"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.OA.24.00213","article-title":"Development of explainable machine learning models to identify patients at risk for 1-year mortality and new distant metastases postendoprosthetic reconstruction for lower extremity bone tumors: a secondary analysis of the PARITY trial","volume":"10","author":"Deng","year":"2025","journal-title":"JB JS Open Access"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1097\/CORR.0000000000002698","article-title":"Does the SORG machine-learning algorithm for extremity metastases generalize to a contemporary cohort of patients? Temporal validation from 2016 to 2020","volume":"481","author":"de Groot","year":"2023","journal-title":"Clin Orthop Relat Res"},{"key":"B30","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1186\/s12885-022-09491-7","article-title":"Machine learning algorithms to estimate 10-year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool","volume":"22","author":"Anderson","year":"2022","journal-title":"BMC Cancer"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbm.2025.100130","article-title":"Prognostic assessment of osteolytic lesions and mechanical properties of bones bearing breast cancer using neural network and finite element analysis","volume":"3","author":"Wang","year":"2025","journal-title":"Mechanobiol Med"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.3233\/CBM-230219","article-title":"Subcutaneous fat predicts bone metastasis in breast cancer: a novel multimodality-based deep learning model","volume":"39","author":"Miao","year":"2024","journal-title":"Cancer biomark"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3390\/jcm15010185","article-title":"Artificial intelligence in intensive care: an overview of systematic reviews with clinical maturity and readiness mapping","volume":"15","author":"\u017berdzi\u0144ski","year":"2025","journal-title":"J Clin Med"},{"key":"B34","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1915919\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T05:33:03Z","timestamp":1787549583000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1915919\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,24]]},"references-count":34,"alternative-id":["10.3389\/fonc.2026.1915919"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1915919","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,24]]},"article-number":"1915919"}}