{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T03:53:32Z","timestamp":1777002812295,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Guangdong Provincial Medical Science and Technology Research Fund Project","award":["A2024269"],"award-info":[{"award-number":["A2024269"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01977-7","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:26:38Z","timestamp":1762165598000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-algorithm radiomics machine learning models integrating ultrasound imaging and inflammation-immune features for hepatic metastases identification"],"prefix":"10.1186","volume":"25","author":[{"given":"Linyong","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaofeng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songhua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaofeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dayou","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"issue":"1","key":"1977_CR1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jncc.2024.01.006","volume":"4","author":"B Han","year":"2024","unstructured":"Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4(1):47\u201353. https:\/\/doi.org\/10.1016\/j.jncc.2024.01.006.","journal-title":"J Natl Cancer Cent"},{"issue":"1","key":"1977_CR2","doi-asserted-by":"publisher","first-page":"8734","DOI":"10.1038\/s41467-024-53095-7","volume":"15","author":"H Hong","year":"2024","unstructured":"Hong H, Eom E, Lee H, Choi S, Choi B, Kim JK. Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics. Nat Commun. 2024;15(1):8734. https:\/\/doi.org\/10.1038\/s41467-024-53095-7.","journal-title":"Nat Commun"},{"issue":"1","key":"1977_CR3","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1038\/s41572-021-00261-6","volume":"7","author":"DI Tsilimigras","year":"2021","unstructured":"Tsilimigras DI, Brodt P, Clavien PA, Muschel RJ, D\u2019Angelica MI, Endo I, et al. Liver metastases. Nat Rev Dis Primers. 2021;7(1):27. https:\/\/doi.org\/10.1038\/s41572-021-00261-6.","journal-title":"Nat Rev Dis Primers"},{"key":"1977_CR4","doi-asserted-by":"publisher","first-page":"101760","DOI":"10.1016\/j.canep.2020.101760","volume":"67","author":"SR Horn","year":"2020","unstructured":"Horn SR, Stoltzfus KC, Lehrer EJ, Dawson LA, Tchelebi L, Gusani NJ, et al. Epidemiology of liver metastases. Cancer Epidemiol. 2020;67:101760. https:\/\/doi.org\/10.1016\/j.canep.2020.101760.","journal-title":"Cancer Epidemiol"},{"issue":"12","key":"1977_CR5","doi-asserted-by":"publisher","first-page":"1584","DOI":"10.1007\/s11547-021-01428-7","volume":"126","author":"V Granata","year":"2021","unstructured":"Granata V, Grassi R, Fusco R, Setola SV, Belli A, Ottaiano A, et al. Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features. Radiol Med. 2021;126(12):1584\u2013600. https:\/\/doi.org\/10.1007\/s11547-021-01428-7.","journal-title":"Radiol Med"},{"issue":"11","key":"1977_CR6","doi-asserted-by":"publisher","first-page":"3662","DOI":"10.1007\/s00261-020-02559-7","volume":"45","author":"M Saleh","year":"2020","unstructured":"Saleh M, Virarkar M, Bura V, Valenzuela R, Javadi S, Szklaruk J, et al. Intrahepatic cholangiocarcinoma: pathogenesis, current staging, and radiological findings. Abdom Radiol (NY). 2020;45(11):3662\u201380. https:\/\/doi.org\/10.1007\/s00261-020-02559-7.","journal-title":"Abdom Radiol (NY)"},{"issue":"5","key":"1977_CR7","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.trecan.2020.11.002","volume":"7","author":"S Kato","year":"2021","unstructured":"Kato S, Alsafar A, Walavalkar V, Hainsworth J, Kurzrock R. Cancer of unknown primary in the molecular era. Trends Cancer. 2021;7(5):465\u201377.","journal-title":"Trends Cancer"},{"issue":"5","key":"1977_CR8","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.1007\/s00261-024-04625-w","volume":"50","author":"T Siu Xiao","year":"2025","unstructured":"Siu Xiao T, Kuon Yeng Escalante CM, Tahmasebi A, Kono Y, Piscaglia F, Wilson SR, et al. Combining CEUS and CT\/MRI LI-RADS major imaging features: diagnostic accuracy for classification of indeterminate liver observations in patients at risk for HCC. Abdom Radiol (NY). 2025;50(5):2066\u201377. https:\/\/doi.org\/10.1007\/s00261-024-04625-w.","journal-title":"Abdom Radiol (NY)"},{"issue":"9","key":"1977_CR9","doi-asserted-by":"publisher","first-page":"2010","DOI":"10.1016\/j.acra.2023.04.030","volume":"30","author":"Y Xu","year":"2023","unstructured":"Xu Y, Ye F, Li L, Yang Y, Ouyang J, Zhou Y, et al. MRI-Based radiomics nomogram for preoperatively differentiating intrahepatic Mass-Forming cholangiocarcinoma from resectable colorectal liver metastases. Acad Radiol. 2023;30(9):2010\u201320. https:\/\/doi.org\/10.1016\/j.acra.2023.04.030.","journal-title":"Acad Radiol"},{"issue":"1","key":"1977_CR10","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s12880-017-0212-x","volume":"17","author":"Z Li","year":"2017","unstructured":"Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017;17(1):42. https:\/\/doi.org\/10.1186\/s12880-017-0212-x.","journal-title":"BMC Med Imaging"},{"issue":"1","key":"1977_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s42492-023-00147-2","volume":"6","author":"H Zhang","year":"2023","unstructured":"Zhang H, Meng Z, Ru J, Meng Y, Wang K. Application and prospects of AI-based radiomics in ultrasound diagnosis. Vis Comput Ind Biomed Art. 2023;6(1):20. https:\/\/doi.org\/10.1186\/s42492-023-00147-2.","journal-title":"Vis Comput Ind Biomed Art"},{"key":"1977_CR12","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.3389\/fonc.2020.01646","volume":"10","author":"Y Peng","year":"2020","unstructured":"Peng Y, Lin P, Wu L, Wan D, Zhao Y, Liang L, et al. Ultrasound-Based radiomics analysis for preoperatively predicting different histopathological subtypes of primary liver cancer. Front Oncol. 2020;10:1646. https:\/\/doi.org\/10.3389\/fonc.2020.01646.","journal-title":"Front Oncol"},{"key":"1977_CR13","doi-asserted-by":"publisher","first-page":"105058","DOI":"10.1016\/j.compbiomed.2021.105058","volume":"141","author":"X Wang","year":"2022","unstructured":"Wang X, Wang S, Yin X, Zheng Y. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med. 2022;141:105058. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105058.","journal-title":"Comput Biol Med"},{"issue":"7","key":"1977_CR14","doi-asserted-by":"publisher","first-page":"4576","DOI":"10.1007\/s00330-020-07562-6","volume":"31","author":"B Mao","year":"2021","unstructured":"Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol. 2021;31(7):4576\u201386. https:\/\/doi.org\/10.1007\/s00330-020-07562-6.","journal-title":"Eur Radiol"},{"issue":"6","key":"1977_CR15","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1002\/jum.15506","volume":"40","author":"H Qin","year":"2021","unstructured":"Qin H, Wu YQ, Lin P, Gao RZ, Li X, Wang XR, et al. Ultrasound Image-Based radiomics: an innovative method to identify primary tumorous sources of liver metastases. J Ultrasound Med. 2021;40(6):1229\u201344. https:\/\/doi.org\/10.1002\/jum.15506.","journal-title":"J Ultrasound Med"},{"issue":"1","key":"1977_CR16","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1186\/s12967-024-05479-y","volume":"22","author":"Y Tang","year":"2024","unstructured":"Tang Y, Su YX, Zheng JM, Zhuo ML, Qian QF, Shen QL, et al. Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer. J Transl Med. 2024;22(1):690. https:\/\/doi.org\/10.1186\/s12967-024-05479-y.","journal-title":"J Transl Med"},{"key":"1977_CR17","doi-asserted-by":"publisher","first-page":"1408700","DOI":"10.3389\/fimmu.2024.1408700","volume":"15","author":"S Tan","year":"2024","unstructured":"Tan S, Zheng Q, Zhang W, Zhou M, Xia C, Feng W. Prognostic value of inflammatory markers NLR, PLR, and LMR in gastric cancer patients treated with immune checkpoint inhibitors: a meta-analysis and systematic review. Front Immunol. 2024;15:1408700. https:\/\/doi.org\/10.3389\/fimmu.2024.1408700.","journal-title":"Front Immunol"},{"key":"1977_CR18","doi-asserted-by":"publisher","first-page":"1211399","DOI":"10.3389\/fimmu.2023.1211399","volume":"14","author":"C Xu","year":"2023","unstructured":"Xu C, Wu F, Du L, Dong Y, Lin S. Significant association between high neutrophil-lymphocyte ratio and poor prognosis in patients with hepatocellular carcinoma: a systematic review and meta-analysis. Front Immunol. 2023;14:1211399. https:\/\/doi.org\/10.3389\/fimmu.2023.1211399.","journal-title":"Front Immunol"},{"key":"1977_CR19","doi-asserted-by":"publisher","first-page":"1297835","DOI":"10.3389\/fneur.2023.1297835","volume":"14","author":"Y Yang","year":"2023","unstructured":"Yang Y, Hu F, Wu S, Huang Z, Wei K, Ma Y, et al. Blood-based biomarkers: diagnostic value in brain tumors (focus on gliomas). Front Neurol. 2023;14:1297835. https:\/\/doi.org\/10.3389\/fneur.2023.1297835.","journal-title":"Front Neurol"},{"key":"1977_CR20","doi-asserted-by":"publisher","first-page":"927491","DOI":"10.3389\/fsurg.2022.927491","volume":"9","author":"Z Ren","year":"2023","unstructured":"Ren Z, Yang J, Liang J, Xu Y, Lu G, Han Y, et al. Monitoring of postoperative neutrophil-to-lymphocyte ratio, D-dimer, and CA153 in: diagnostic value for recurrent and metastatic breast cancer. Front Surg. 2023;9:927491. https:\/\/doi.org\/10.3389\/fsurg.2022.927491.","journal-title":"Front Surg"},{"issue":"11","key":"1977_CR21","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.1158\/0008-5472","volume":"82","author":"W Mu","year":"2022","unstructured":"Mu W, Schabath MB, Gillies RJ. Images are data: challenges and opportunities in the clinical translation of radiomics. Cancer Res. 2022;82(11):2066\u20138. https:\/\/doi.org\/10.1158\/0008-5472.","journal-title":"Cancer Res"},{"issue":"2","key":"1977_CR22","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1007\/s00330-022-09109-3","volume":"33","author":"Q Chen","year":"2023","unstructured":"Chen Q, Shao J, Xue T, et al. Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer. Eur Radiol. 2023;33(2):947\u201358. https:\/\/doi.org\/10.1007\/s00330-022-09109-3.","journal-title":"Eur Radiol"},{"key":"1977_CR23","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.jad.2024.05.061","volume":"359","author":"B Chen","year":"2024","unstructured":"Chen B, Sun X, Huang H, Feng C, Chen W, Wu D. An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes. J Affect Disord. 2024;359:22\u201332. https:\/\/doi.org\/10.1016\/j.jad.2024.05.061.","journal-title":"J Affect Disord"},{"issue":"Pt 1","key":"1977_CR24","doi-asserted-by":"publisher","first-page":"137039","DOI":"10.1016\/j.chemosphere.2022.137039","volume":"311","author":"X Li","year":"2023","unstructured":"Li X, Zhao Y, Zhang D, Kuang L, Huang H, Chen W, et al. Development of an interpretable machine learning model associated with heavy metals\u2019 exposure to identify coronary heart disease among US adults via SHAP: findings of the US NHANES from 2003 to 2018. Chemosphere. 2023;311(Pt 1):137039. https:\/\/doi.org\/10.1016\/j.chemosphere.2022.137039.","journal-title":"Chemosphere"},{"issue":"1","key":"1977_CR25","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1111\/cas.15173","volume":"113","author":"I Hoshino","year":"2022","unstructured":"Hoshino I, Yokota H, Iwatate Y, Mori Y, Kuwayama N, Ishige F, et al. Prediction of the differences in tumor mutation burden between primary and metastatic lesions by radiogenomics. Cancer Sci. 2022;113(1):229\u201339. https:\/\/doi.org\/10.1111\/cas.15173.","journal-title":"Cancer Sci"},{"issue":"1","key":"1977_CR26","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/diagnostics9010004","volume":"9","author":"A Saini","year":"2018","unstructured":"Saini A, Breen I, Pershad Y, Naidu S, Knuttinen MG, Alzubaidi S, et al. Radiogenomics and radiomics in liver cancers. Diagnostics (Basel). 2018;9(1):4. https:\/\/doi.org\/10.3390\/diagnostics9010004.","journal-title":"Diagnostics (Basel)"},{"issue":"22","key":"1977_CR27","doi-asserted-by":"publisher","first-page":"5389","DOI":"10.3390\/cancers15225389","volume":"15","author":"HJ Jang","year":"2023","unstructured":"Jang HJ, Go JH, Kim Y, Lee SH. Deep learning for the pathologic diagnosis of hepatocellular Carcinoma, Cholangiocarcinoma, and metastatic colorectal cancer. Cancers (Basel). 2023;15(22):5389. https:\/\/doi.org\/10.3390\/cancers15225389.","journal-title":"Cancers (Basel)"},{"key":"1977_CR28","doi-asserted-by":"publisher","first-page":"111297","DOI":"10.1016\/j.ejrad.2024.111297","volume":"171","author":"C Maino","year":"2024","unstructured":"Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, et al. Radiomics and liver: where we are and where we are headed? Eur J Radiol. 2024;171:111297. https:\/\/doi.org\/10.1016\/j.ejrad.2024.111297.","journal-title":"Eur J Radiol"},{"issue":"24","key":"1977_CR29","doi-asserted-by":"publisher","first-page":"e14834","DOI":"10.1097\/MD.0000000000014834","volume":"98","author":"C Kumarasamy","year":"2019","unstructured":"Kumarasamy C, Sabarimurugan S, Madurantakam RM, Lakhotiya K, Samiappan S, Baxi S, et al. Prognostic significance of blood inflammatory biomarkers NLR, PLR, and LMR in cancer-A protocol for systematic review and meta-analysis. Med (Baltim). 2019;98(24):e14834. https:\/\/doi.org\/10.1097\/MD.0000000000014834.","journal-title":"Med (Baltim)"},{"issue":"1","key":"1977_CR30","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s12885-022-09223-x","volume":"22","author":"C Ma","year":"2022","unstructured":"Ma C, Li R, Yu R, Guo J, Xu J, Yuan X, et al. Predictive value of preoperative platelet-to-albumin ratio and Apolipoprotein B-to-apolipoprotein A1 ratio for osteosarcoma in children and adolescents: a retrospective study of 118 cases. BMC Cancer. 2022;22(1):113. https:\/\/doi.org\/10.1186\/s12885-022-09223-x.","journal-title":"BMC Cancer"},{"key":"1977_CR31","doi-asserted-by":"publisher","first-page":"897597","DOI":"10.3389\/fphar.2022.897597","volume":"13","author":"Y Feng","year":"2022","unstructured":"Feng Y, Song F, Zhang P, Fan G, Zhang T, Zhao X, et al. Prediction of EGFR mutation status in Non-Small cell lung cancer based on ensemble learning. Front Pharmacol. 2022;13:897597. https:\/\/doi.org\/10.3389\/fphar.2022.897597.","journal-title":"Front Pharmacol"},{"key":"1977_CR32","doi-asserted-by":"publisher","first-page":"107327482210929","DOI":"10.1177\/10732748221092926","volume":"29","author":"Y Liu","year":"2022","unstructured":"Liu Y, Zhou J, Wu J, Wang W, Wang X, Guo J, et al. Development and validation of machine learning models to predict epidermal growth factor receptor mutation in Non-Small cell lung cancer: A Multi-Center retrospective radiomics study. Cancer Control. 2022;29:10732748221092926. https:\/\/doi.org\/10.1177\/10732748221092926.","journal-title":"Cancer Control"},{"issue":"1","key":"1977_CR33","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s12944-023-01988-9","volume":"23","author":"L Dai","year":"2024","unstructured":"Dai L, Yuan W, Jiang R, Zhan Z, Zhang L, Xu X, et al. Machine learning-based integration identifies the ferroptosis hub genes in nonalcoholic steatohepatitis. Lipids Health Dis. 2024;23(1):23. https:\/\/doi.org\/10.1186\/s12944-023-01988-9.","journal-title":"Lipids Health Dis"},{"issue":"3","key":"1977_CR34","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/s00432-024-05650-4","volume":"150","author":"S Li","year":"2024","unstructured":"Li S, Mi T, Jin L, Liu Y, Zhang Z, Wang J, et al. Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma. J Cancer Res Clin Oncol. 2024;150(3):148. https:\/\/doi.org\/10.1007\/s00432-024-05650-4.","journal-title":"J Cancer Res Clin Oncol"},{"issue":"3","key":"1977_CR35","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1007\/s00330-020-07174-0","volume":"31","author":"M Ligero","year":"2021","unstructured":"Ligero M, Jordi-Ollero O, Bernatowicz K, Garcia-Ruiz A, Delgado-Mu\u00f1oz E, Leiva D, et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur Radiol. 2021;31(3):1460\u201370. https:\/\/doi.org\/10.1007\/s00330-020-07174-0.","journal-title":"Eur Radiol"},{"issue":"1","key":"1977_CR36","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1038\/s41598-023-50984-7","volume":"14","author":"J Lu","year":"2024","unstructured":"Lu J, Ji X, Liu X, Jiang Y, Li G, Fang P, et al. Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC. Sci Rep. 2024;14(1):446. https:\/\/doi.org\/10.1038\/s41598-023-50984-7.","journal-title":"Sci Rep"},{"key":"1977_CR37","doi-asserted-by":"publisher","first-page":"102490","DOI":"10.1016\/j.compmedimag.2024.102490","volume":"120","author":"Y Shen","year":"2025","unstructured":"Shen Y, Chen L, Liu J, Chen H, Wang C, Ding H, et al. PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease. Comput Med Imaging Graph. 2025;120:102490. https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102490.","journal-title":"Comput Med Imaging Graph"},{"issue":"6","key":"1977_CR38","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1016\/j.jhep.2019.02.023","volume":"70","author":"X Xu","year":"2019","unstructured":"Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70(6):1133\u201344. https:\/\/doi.org\/10.1016\/j.jhep.2019.02.023.","journal-title":"J Hepatol"},{"issue":"1","key":"1977_CR39","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s12880-024-01542-8","volume":"25","author":"YT Peng","year":"2025","unstructured":"Peng YT, Pang JS, Lin P, Chen JM, Wen R, Liu CW, et al. Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers. BMC Med Imaging. 2025;25(1):4. https:\/\/doi.org\/10.1186\/s12880-024-01542-8.","journal-title":"BMC Med Imaging"},{"key":"1977_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-023-15627-z","volume":"15","author":"RV Manjunath","year":"2023","unstructured":"Manjunath RV, Ghanshala A, Kwadiki K. Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images. Multimed Tools Appl. 2023;15:1\u201318. https:\/\/doi.org\/10.1007\/s11042-023-15627-z.","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"1977_CR41","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s40644-021-00387-6","volume":"21","author":"W Sun","year":"2021","unstructured":"Sun W, Liu S, Guo J, Liu S, Hao D, Hou F, et al. A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours. Cancer Imaging. 2021;21(1):20. https:\/\/doi.org\/10.1186\/s40644-021-00387-6.","journal-title":"Cancer Imaging"},{"issue":"1","key":"1977_CR42","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1186\/s12880-024-01450-x","volume":"24","author":"A Lin","year":"2024","unstructured":"Lin A, Zhang H, Wang Y, Cui Q, Zhu K, Zhou D, et al. Radiomics based on MRI to predict recurrent L4-5 disc herniation after percutaneous endoscopic lumbar discectomy. BMC Med Imaging. 2024;24(1):273. https:\/\/doi.org\/10.1186\/s12880-024-01450-x.","journal-title":"BMC Med Imaging"},{"key":"1977_CR43","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.2147\/JHC.S423549","volume":"11","author":"L Ren","year":"2024","unstructured":"Ren L, Chen DB, Yan X, She S, Yang Y, Zhang X, et al. Bridging the gap between imaging and molecular characterization: current Understanding of radiomics and radiogenomics in hepatocellular carcinoma. J Hepatocell Carcinoma. 2024;11:2359\u201372. https:\/\/doi.org\/10.2147\/JHC.S423549.","journal-title":"J Hepatocell Carcinoma"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01977-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01977-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01977-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:26:39Z","timestamp":1762165599000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01977-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,3]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1977"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01977-7","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,3]]},"assertion":[{"value":"26 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 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":"The study was conducted in accordance with the Declaration of Helsinki. This retrospective study was approved by the Ethics Committee of Maoming People\u2019s Hospital (PJ2025MI-K051-01), and the requirement for written informed consent was waived.","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":"438"}}