{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:21:47Z","timestamp":1791228107936,"version":"4.1.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and purpose<\/jats:title>\n                    <jats:p>Peritoneal metastasis significantly impacts prognosis and treatment strategies in ovarian cancer. Traditional imaging techniques have limited sensitivity in preoperative detection. Radiomics-based machine learning models offer a promising non-invasive approach to improve diagnostic accuracy. This study systematically reviews and meta-analyzes their predictive performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A systematic search was conducted up to June 2025 for studies that developed and validated machine learning models based on radiomic features for the prediction of peritoneal metastasis in ovarian cancer. Quality of included studies was evaluated using the METRICS and QUADAS-2 tool. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated via bivariate random-effects meta-analysis. Heterogeneity, sensitivity, and publication bias analyses were also conducted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six studies were included in the systematic review and qualitative synthesis, with five studies comprising 448 individual participants derived exclusively from validation cohorts meeting criteria for meta-analysis. Radiomics models yielded a pooled AUC of 0.81 (95% CI: 0.71\u20130.88), accompanied by a sensitivity of 73% and specificity of 77%. Clinical models demonstrated a pooled AUC of 0.82 (95% CI: 0.79\u20130.85). The pooled AUC for the combined clinical\u2013radiomics models was 0.87 (95% CI: 0.83\u20130.89). Methodological quality assessed via METRICS ranged from 64.5% to 86.3%, with a mean score of 77.6%. The QUADAS-2 assessment indicated a low overall risk of bias, with minor concerns noted in the patient selection and index test domains. No significant heterogeneity, publication bias, or outliers were detected.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Radiomics-based machine learning models demonstrate potential as tools for the prediction of peritoneal metastasis in ovarian cancer and may assist in preoperative risk stratification. Further large-scale, multicenter prospective studies with standardized methodologies and external validation are necessary to confirm clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-025-02068-3","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T18:05:54Z","timestamp":1764698754000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomics-based machine learning in the prediction of peritoneal metastasis in ovarian cancer: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7225-567X","authenticated-orcid":false,"given":"Mohsen","family":"Salimi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6252-8101","authenticated-orcid":false,"given":"Pouria","family":"Vadipour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0984-4141","authenticated-orcid":false,"given":"Ali","family":"Abdolizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7793-7972","authenticated-orcid":false,"given":"Farzad","family":"Fayedeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1340-0949","authenticated-orcid":false,"given":"Sharareh","family":"Seifi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"2068_CR1","doi-asserted-by":"publisher","unstructured":"Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. Cancer J Clin. 2017;67(1):7\u201330. https:\/\/doi.org\/10.3322\/caac.21387.","DOI":"10.3322\/caac.21387"},{"issue":"8","key":"2068_CR2","doi-asserted-by":"publisher","first-page":"512","DOI":"10.6004\/jnccn.2024.0052","volume":"22","author":"J Liu","year":"2024","unstructured":"Liu J, Berchuck A, Backes FJ, et al. NCCN Guidelines\u00ae insights: ovarian Cancer\/Fallopian tube Cancer\/Primary peritoneal Cancer, version 3.2024. J Natl Compr Canc Netw. 2024;22(8):512\u20139. https:\/\/doi.org\/10.6004\/jnccn.2024.0052.","journal-title":"J Natl Compr Canc Netw"},{"key":"2068_CR3","doi-asserted-by":"publisher","unstructured":"Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12\u201349. https:\/\/doi.org\/10.3322\/caac.21820.","DOI":"10.3322\/caac.21820"},{"issue":"10177","key":"2068_CR4","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1016\/S0140-6736(18)32552-2","volume":"393","author":"S Lheureux","year":"2019","unstructured":"Lheureux S, Gourley C, Vergote I, Oza AM. Epithelial ovarian cancer. Lancet. 2019;393(10177):1240\u201353. https:\/\/doi.org\/10.1016\/S0140-6736(18)32552-2.","journal-title":"Lancet"},{"issue":"S1","key":"2068_CR5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1002\/ijgo.13878","volume":"155","author":"JS Berek","year":"2021","unstructured":"Berek JS, Renz M, Kehoe S, Kumar L, Friedlander M. Cancer of the ovary, fallopian tube, and peritoneum: 2021 update. Int J Gynecol Obstet. 2021;155(S1):61\u201385. https:\/\/doi.org\/10.1002\/ijgo.13878.","journal-title":"Int J Gynecol Obstet"},{"issue":"3","key":"2068_CR6","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ygyno.2014.04.013","volume":"133","author":"DG Mutch","year":"2014","unstructured":"Mutch DG, Prat J. 2014 FIGO staging for ovarian, fallopian tube and peritoneal cancer. Gynecol Oncol. 2014;133(3):401\u20134. https:\/\/doi.org\/10.1016\/j.ygyno.2014.04.013.","journal-title":"Gynecol Oncol"},{"issue":"11","key":"2068_CR7","doi-asserted-by":"publisher","first-page":"3831","DOI":"10.3390\/jcm12113831","volume":"12","author":"C Ronsini","year":"2023","unstructured":"Ronsini C, Pasanisi F, Molitierno R, et al. Minimally invasive staging of Early-Stage epithelial ovarian cancer versus open surgery in terms of feasibility and safety: A systematic review and Meta-Analysis. J Clin Med. 2023;12(11):3831. https:\/\/doi.org\/10.3390\/jcm12113831.","journal-title":"J Clin Med"},{"issue":"10","key":"2068_CR8","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.annonc.2023.07.011","volume":"34","author":"A Gonz\u00e1lez-Mart\u00edn","year":"2023","unstructured":"Gonz\u00e1lez-Mart\u00edn A, Harter P, Leary A, et al. Newly diagnosed and relapsed epithelial ovarian cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up\u2606. Ann Oncol. 2023;34(10):833\u201348. https:\/\/doi.org\/10.1016\/j.annonc.2023.07.011.","journal-title":"Ann Oncol"},{"issue":"8","key":"2068_CR9","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.3390\/cancers16081467","volume":"16","author":"AC Tsili","year":"2024","unstructured":"Tsili AC, Alexiou G, Tzoumpa M, Siempis T, Argyropoulou MI. Imaging of peritoneal metastases in ovarian cancer using MDCT, MRI, and FDG PET\/CT: A systematic review and Meta-Analysis. Cancers. 2024;16(8):1467. https:\/\/doi.org\/10.3390\/cancers16081467.","journal-title":"Cancers"},{"key":"2068_CR10","doi-asserted-by":"publisher","first-page":"610742","DOI":"10.3389\/fonc.2021.610742","volume":"11","author":"Y Ai","year":"2021","unstructured":"Ai Y, Zhang J, Jin J, Zhang J, Zhu H, Jin X. Preoperative prediction of metastasis for ovarian cancer based on computed tomography radiomics features and clinical factors. Front Oncol. 2021;11:610742. https:\/\/doi.org\/10.3389\/fonc.2021.610742.","journal-title":"Front Oncol"},{"issue":"2","key":"2068_CR11","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1093\/jjco\/hyaa145","volume":"51","author":"T Onda","year":"2021","unstructured":"Onda T, Tanaka YO, Kitai S, et al. Stage III disease of ovarian, tubal and peritoneal cancers can be accurately diagnosed with pre-operative CT. Japan clinical oncology group study JCOG0602. Jpn J Clin Oncol. 2021;51(2):205\u201312. https:\/\/doi.org\/10.1093\/jjco\/hyaa145.","journal-title":"Jpn J Clin Oncol"},{"issue":"1","key":"2068_CR12","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ygyno.2021.01.013","volume":"161","author":"R Tozzi","year":"2021","unstructured":"Tozzi R, Traill Z, Valenti G, Ferrari F, Gubbala K, Campanile RG. A prospective study on the diagnostic pathway of patients with stage IIIC-IV ovarian cancer: exploratory laparoscopy (EXL)\u2009+\u2009CT scan VS. CT scan. Gynecol Oncol. 2021;161(1):188\u201393. https:\/\/doi.org\/10.1016\/j.ygyno.2021.01.013.","journal-title":"Gynecol Oncol"},{"issue":"5","key":"2068_CR13","doi-asserted-by":"publisher","first-page":"2712","DOI":"10.1007\/s00330-024-11124-5","volume":"35","author":"V Vandecaveye","year":"2025","unstructured":"Vandecaveye V, Rousset P, Nougaret S, et al. Imaging of peritoneal metastases of ovarian and colorectal cancer: joint recommendations of ESGAR, ESUR, PSOGI, and EANM. Eur Radiol. 2025;35(5):2712\u201322. https:\/\/doi.org\/10.1007\/s00330-024-11124-5.","journal-title":"Eur Radiol"},{"issue":"6","key":"2068_CR14","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.3390\/jcm11061524","volume":"11","author":"T Gagliardi","year":"2022","unstructured":"Gagliardi T, Adejolu M, deSouza NM. Diffusion-Weighted magnetic resonance imaging in ovarian cancer: exploiting strengths and Understanding limitations. J Clin Med. 2022;11(6):1524. https:\/\/doi.org\/10.3390\/jcm11061524.","journal-title":"J Clin Med"},{"key":"2068_CR15","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ejrad.2019.03.007","volume":"114","author":"MP Engbersen","year":"2019","unstructured":"Engbersen MP, Van\u2019 T Sant I, Lok C, et al. MRI with diffusion-weighted imaging to predict feasibility of complete cytoreduction with the peritoneal cancer index (PCI) in advanced stage ovarian cancer patients. Eur J Radiol. 2019;114:146\u201351. https:\/\/doi.org\/10.1016\/j.ejrad.2019.03.007.","journal-title":"Eur J Radiol"},{"issue":"1125","key":"2068_CR16","doi-asserted-by":"publisher","first-page":"20210117","DOI":"10.1259\/bjr.20210117","volume":"94","author":"MP Engbersen","year":"2021","unstructured":"Engbersen MP, Van Driel W, Lambregts D, Lahaye M. The role of CT, PET-CT, and MRI in ovarian cancer. Br J Radiol. 2021;94(1125):20210117. https:\/\/doi.org\/10.1259\/bjr.20210117.","journal-title":"Br J Radiol"},{"issue":"1132","key":"2068_CR17","doi-asserted-by":"publisher","first-page":"20210217","DOI":"10.1259\/bjr.20210217","volume":"95","author":"JW Power","year":"2022","unstructured":"Power JW, Dempsey PJ, Yates A, et al. Peritoneal malignancy: anatomy, pathophysiology and an update on modern day imaging. Br J Radiol. 2022;95(1132):20210217. https:\/\/doi.org\/10.1259\/bjr.20210217.","journal-title":"Br J Radiol"},{"issue":"1","key":"2068_CR18","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1102\/1470-7330.2011.0016","volume":"11","author":"CM Patel","year":"2011","unstructured":"Patel CM, Sahdev A, Reznek RH. CT, MRI and PET imaging in peritoneal malignancy. Cancer Imaging. 2011;11(1):123\u201339. https:\/\/doi.org\/10.1102\/1470-7330.2011.0016.","journal-title":"Cancer Imaging"},{"issue":"6","key":"2068_CR19","doi-asserted-by":"publisher","first-page":"3498","DOI":"10.1016\/j.acra.2024.12.074","volume":"32","author":"Y Zhou","year":"2025","unstructured":"Zhou Y, Duan Y, Zhu Q, Li S, Zhang C. A multimodal model based on transvaginal Ultrasound-Based radiomics to predict the risk of peritoneal metastasis in ovarian cancer: A multicenter study. Acad Radiol. 2025;32(6):3498\u2013510. https:\/\/doi.org\/10.1016\/j.acra.2024.12.074.","journal-title":"Acad Radiol"},{"issue":"2","key":"2068_CR20","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than Pictures, they are data. Radiology. 2016;278(2):563\u201377. https:\/\/doi.org\/10.1148\/radiol.2015151169.","journal-title":"Radiology"},{"key":"2068_CR21","doi-asserted-by":"publisher","unstructured":"Ren XYY, Jia J. Multiparameter MRI radiomics model predicts preoperative peritoneal carcinomatosis in ovarian cancer. Front Oncol. 2021;11. https:\/\/doi.org\/10.3389\/fonc.2021.765652.","DOI":"10.3389\/fonc.2021.765652"},{"issue":"4","key":"2068_CR22","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1001\/jama.2017.19163","volume":"319","author":"MDF McInnes","year":"2018","unstructured":"McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and Meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA. 2018;319(4):388. https:\/\/doi.org\/10.1001\/jama.2017.19163.","journal-title":"JAMA"},{"issue":"3","key":"2068_CR23","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1002\/bimj.200710415","volume":"50","author":"MD Ruopp","year":"2008","unstructured":"Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden index and optimal Cut-Point estimated from observations affected by a lower limit of detection. Biometrical J Biometrische Z. 2008;50(3):419\u201330. https:\/\/doi.org\/10.1002\/bimj.200710415.","journal-title":"Biometrical J Biometrische Z"},{"issue":"8","key":"2068_CR24","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529. https:\/\/doi.org\/10.7326\/0003-4819-155-8-201110180-00009.","journal-title":"Ann Intern Med"},{"key":"2068_CR25","doi-asserted-by":"publisher","unstructured":"Kocak B, Tugba Akinci D\u2019Antonoli, Mercaldo N, et al. METhodological radiomics score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights into Imaging. 2024;15(1). https:\/\/doi.org\/10.1186\/s13244-023-01572-w.","DOI":"10.1186\/s13244-023-01572-w"},{"issue":"7414","key":"2068_CR26","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1136\/bmj.327.7414.557","volume":"327","author":"JPT Higgins","year":"2003","unstructured":"Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557\u201360. https:\/\/doi.org\/10.1136\/bmj.327.7414.557.","journal-title":"BMJ"},{"issue":"9","key":"2068_CR27","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","volume":"58","author":"JJ Deeks","year":"2005","unstructured":"Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58(9):882\u201393. https:\/\/doi.org\/10.1016\/j.jclinepi.2005.01.016.","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"2068_CR28","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1002\/jmri.28761","volume":"59","author":"M Wei","year":"2023","unstructured":"Wei M, Zhang Y, Ding C, et al. Associating peritoneal metastasis with T2-Weighted MRI images in epithelial ovarian cancer using deep learning and radiomics: a multicenter study. J Magn Reson Imaging. 2023;59(1):122\u201331. https:\/\/doi.org\/10.1002\/jmri.28761.","journal-title":"J Magn Reson Imaging"},{"issue":"5","key":"2068_CR29","doi-asserted-by":"publisher","first-page":"1918","DOI":"10.1016\/j.acra.2023.11.032","volume":"31","author":"J Li","year":"2024","unstructured":"Li J, Zhang J, Wang F, Ma J, Cui S, Ye Z. CTBased radiomics for the preoperative prediction of occult peritoneal metastasis in epithelial ovarian cancers. Acad Radiol. 2024;31(5):1918\u201330. https:\/\/doi.org\/10.1016\/j.acra.2023.11.032.","journal-title":"Acad Radiol"},{"key":"2068_CR30","doi-asserted-by":"publisher","unstructured":"Wang X, Wei M, Chen Y, et al. Intratumoral and peritumoral MRI-based radiomics for predicting extrapelvic peritoneal metastasis in epithelial ovarian cancer. Insights into Imaging. 2024;15(1). https:\/\/doi.org\/10.1186\/s13244-024-01855-w.","DOI":"10.1186\/s13244-024-01855-w"},{"issue":"11","key":"2068_CR31","doi-asserted-by":"publisher","first-page":"8438","DOI":"10.1007\/s00330-021-08004-7","volume":"31","author":"XL Song","year":"2021","unstructured":"Song XL, Ren JL, Yao TY, Zhao D, Niu J. Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer. Eur Radiol. 2021;31(11):8438\u201346. https:\/\/doi.org\/10.1007\/s00330-021-08004-7.","journal-title":"Eur Radiol"},{"issue":"1","key":"2068_CR32","doi-asserted-by":"publisher","first-page":"541842","DOI":"10.1155\/2012\/541842","volume":"2012","author":"E Halkia","year":"2012","unstructured":"Halkia E, Spiliotis J, Sugarbaker P. Diagnosis and management of peritoneal metastases from ovarian cancer. Gastroenterol Res Pract. 2012;2012(1):541842. https:\/\/doi.org\/10.1155\/2012\/541842.","journal-title":"Gastroenterol Res Pract"},{"issue":"1","key":"2068_CR33","doi-asserted-by":"publisher","first-page":"60","DOI":"10.3390\/cancers14010060","volume":"14","author":"L Roth","year":"2021","unstructured":"Roth L, Russo L, Ulugoel S, et al. Peritoneal metastasis: current status and treatment options. Cancers. 2021;14(1):60. https:\/\/doi.org\/10.3390\/cancers14010060.","journal-title":"Cancers"},{"issue":"10","key":"2068_CR34","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.diii.2022.05.003","volume":"103","author":"S Nougaret","year":"2022","unstructured":"Nougaret S, Sadowski E, Lakhman Y, et al. The bumpy road of peritoneal metastases in ovarian cancer. Diagn Interv Imaging. 2022;103(10):448\u201359. https:\/\/doi.org\/10.1016\/j.diii.2022.05.003.","journal-title":"Diagn Interv Imaging"},{"key":"2068_CR35","doi-asserted-by":"publisher","unstructured":"Zeng S, Wang XL, Yang H. Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer. Military Med Res. 2024;11(1). https:\/\/doi.org\/10.1186\/s40779-024-00580-1.","DOI":"10.1186\/s40779-024-00580-1"},{"key":"2068_CR36","doi-asserted-by":"publisher","unstructured":"Huang ML, Ren J, Jin ZY, et al. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights Imaging. 2023;14(1). https:\/\/doi.org\/10.1186\/s13244-023-01464-z.","DOI":"10.1186\/s13244-023-01464-z"},{"issue":"1","key":"2068_CR37","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","volume":"3","author":"AJ Vickers","year":"2019","unstructured":"Vickers AJ, Van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019;3(1):18. https:\/\/doi.org\/10.1186\/s41512-019-0064-7.","journal-title":"Diagn Progn Res"},{"key":"2068_CR38","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj2023078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons, Dhiman P, et al. TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. https:\/\/doi.org\/10.1136\/bmj2023078378.","journal-title":"BMJ"},{"issue":"4","key":"2068_CR39","doi-asserted-by":"publisher","first-page":"e240300","DOI":"10.1148\/ryai.240300","volume":"6","author":"AS Tejani","year":"2024","unstructured":"Tejani AS, Klontzas ME, Gatti AA, et al. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiology: Artif Intell. 2024;6(4):e240300. https:\/\/doi.org\/10.1148\/ryai.240300.","journal-title":"Radiology: Artif Intell"},{"issue":"10","key":"2068_CR40","doi-asserted-by":"publisher","first-page":"4748","DOI":"10.1007\/s00261-025-04892-1","volume":"50","author":"M Salimi","year":"2025","unstructured":"Salimi M, Vadipour P, Houshi S, et al. MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis. Abdom Radiol. 2025;50:4748\u201371. https:\/\/doi.org\/10.1007\/s00261-025-04892-1.","journal-title":"Abdom Radiol"},{"key":"2068_CR41","doi-asserted-by":"publisher","unstructured":"Salimi M, Vadipour P, Khosravi A, et al. CT-Based radiomics for predicting PD-L1 expression in non-small cell lung cancer: a systematic review and meta-analysis. Acad Radiol. 2025;32(11):6913\u201328. https:\/\/doi.org\/10.1016\/j.acra.2025.05.024.","DOI":"10.1016\/j.acra.2025.05.024"},{"issue":"7","key":"2068_CR42","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1016\/j.spinee.2020.02.016","volume":"20","author":"VE Staartjes","year":"2020","unstructured":"Staartjes VE, Kernbach JM. Significance of external validation in clinical machine learning: let loose too early? Spine J. 2020;20(7):1159\u201360. https:\/\/doi.org\/10.1016\/j.spinee.2020.02.016.","journal-title":"Spine J"},{"issue":"30","key":"2068_CR43","doi-asserted-by":"publisher","first-page":"50015","DOI":"10.18632\/oncotarget.18388","volume":"8","author":"Q Yuan","year":"2017","unstructured":"Yuan Q, Song J, Yang W, et al. The effect of CA125 on metastasis of ovarian cancer: old marker new function. Oncotarget. 2017;8(30):50015\u201322. https:\/\/doi.org\/10.18632\/oncotarget.18388.","journal-title":"Oncotarget"},{"issue":"13","key":"2068_CR44","doi-asserted-by":"publisher","first-page":"10479","DOI":"10.3390\/ijms241310479","volume":"24","author":"E Anastasi","year":"2023","unstructured":"Anastasi E, Farina A, Granato T, et al. Recent insight about HE4 role in ovarian cancer oncogenesis. IJMS. 2023;24(13):10479. https:\/\/doi.org\/10.3390\/ijms241310479.","journal-title":"IJMS"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02068-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-02068-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02068-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T14:55:36Z","timestamp":1781621736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12880-025-02068-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2068"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-02068-3","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]},"assertion":[{"value":"28 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"As this study is a review and does not involve direct participation of human or animal subjects, ethical approval and Institutional Review Board (IRB) clearance were not required in accordance with the policies of our institution.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}