{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T07:38:00Z","timestamp":1783496280178,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Objectives<\/jats:title>\n            <jats:p>The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative gadobenate-enhanced (Gd-BOPTA) magnetic resonance imaging (MRI) using multilayer perceptron (MLP) deep learning to predict vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) patients.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A total of 230 patients with histopathologically confirmed HCC who underwent preoperative Gd-BOPTA MRI before hepatectomy were retrospectively enrolled from three hospitals (144, 54, and 32 in training, test, and validation set, respectively). Univariate and multivariate logistic regression analyses were used to determine independent clinicoradiological predictors significantly associated with VETC, which then constituted the clinicoradiological model. Regions of interest (ROIs) included four modes, intratumoral (Tumor), peritumoral area\u2009\u2264\u20092\u00a0mm (Peri2mm), intratumoral\u2009+\u2009peritumoral area\u2009\u2264\u20092\u00a0mm (Tumor\u2009+\u2009Peri2mm) and intratumoral integrated with peritumoral\u2009\u2264\u20092\u00a0mm as a whole (TumorPeri2mm). A total of 7322 radiomics features were extracted respectively for ROI(Tumor), ROI(Peri2mm), ROI(TumorPeri2mm) and 14644 radiomics features for ROI(Tumor\u2009+\u2009Peri2mm). Least absolute shrinkage and selection operator (LASSO) and univariate logistic regression analysis were used to select the important features. Seven different machine learning classifiers respectively combined the radiomics signatures selected from four ROIs to constitute different models, and compare the performance between them in three sets and then select the optimal combination to become the radiomics model we need. Then a radiomics score (rad-score) was generated, which combined significant clinicoradiological predictors to constituted the fusion model through multivariate logistic regression analysis. After comparing the performance of the three models using area under receiver operating characteristic curve (AUC), integrated discrimination index (IDI) and net reclassification index (NRI), choose the optimal predictive model for VETC prediction.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Result<\/jats:title>\n            <jats:p>Arterial peritumoral enhancement and peritumoral hypointensity on hepatobiliary phase (HBP) were independent risk factors for VETC, and constituted the Radiology model, without any clinical variables. Arterial peritumoral enhancement defined as the enhancement outside the tumor boundary in the late stage of arterial phase or early stage of portal phase, extensive contact with the tumor edge, which becomes isointense during the DP. MLP deep learning algorithm integrated radiomics features selected from ROI TumorPeri2mm was the best combination, which constituted the radiomics model (MLP model). A MLP score (MLP_score) was calculated then, which combining the two radiology features composed the fusion model (Radiology MLP model), with AUCs of 0.871, 0.894, 0.918 in the training, test and validation sets. Compared with the two models aforementioned, the Radiology MLP model demonstrated a 33.4%-131.3% improvement in NRI and a 9.3%-50% improvement in IDI, showing better discrimination, calibration and clinical usefulness in three sets, which was selected as the optimal predictive model.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>We mainly developed a fusion model (Radiology MLP model) that integrated radiology and radiomics features using MLP deep learning algorithm to predict vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) patients, which yield an incremental value over the radiology and the MLP model.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s40644-025-00895-9","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T10:57:28Z","timestamp":1751885848000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study"],"prefix":"10.1186","volume":"25","author":[{"given":"Mengting","family":"Gu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huilin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruilin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningyang","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanmin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"issue":"3","key":"895_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u201349.","journal-title":"CA Cancer J Clin"},{"issue":"10127","key":"895_CR2","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1016\/S0140-6736(18)30010-2","volume":"391","author":"A Forner","year":"2018","unstructured":"Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet. 2018;391(10127):1301\u201314.","journal-title":"Lancet"},{"issue":"6","key":"895_CR3","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, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70(6):1133\u201344.","journal-title":"J Hepatol"},{"issue":"2","key":"895_CR4","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1002\/hep.29913","volume":"68","author":"JA Marrero","year":"2018","unstructured":"Marrero JA, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018;68(2):723\u201350.","journal-title":"Hepatology"},{"issue":"15","key":"895_CR5","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.1056\/NEJMra1713263","volume":"380","author":"A Villanueva","year":"2019","unstructured":"Villanueva A. Hepatocellular Carcinoma. N Engl J Med. 2019;380(15):1450\u201362.","journal-title":"N Engl J Med"},{"issue":"1","key":"895_CR6","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1002\/hep.30814","volume":"71","author":"SL Renne","year":"2020","unstructured":"Renne SL, et al. Vessels Encapsulating Tumor Clusters (VETC) Is a Powerful Predictor of Aggressive Hepatocellular Carcinoma. Hepatology. 2020;71(1):183\u201395.","journal-title":"Hepatology"},{"issue":"2","key":"895_CR7","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1002\/hep.27760","volume":"62","author":"JH Fang","year":"2015","unstructured":"Fang JH, et al. A novel vascular pattern promotes metastasis of hepatocellular carcinoma in an epithelial-mesenchymal transition-independent manner. Hepatology. 2015;62(2):452\u201365.","journal-title":"Hepatology"},{"issue":"4","key":"895_CR8","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.gassur.2024.02.004","volume":"28","author":"M Wang","year":"2024","unstructured":"Wang M, et al. Prediction of vessels encapsulating tumor clusters pattern and prognosis of hepatocellular carcinoma based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging. J Gastrointest Surg. 2024;28(4):442\u201350.","journal-title":"J Gastrointest Surg"},{"issue":"3","key":"895_CR9","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1002\/hep.30366","volume":"70","author":"JH Fang","year":"2019","unstructured":"Fang JH, et al. Vessels That Encapsulate Tumor Clusters (VETC) Pattern Is a Predictor of Sorafenib Benefit in Patients with Hepatocellular Carcinoma. Hepatology. 2019;70(3):824\u201339.","journal-title":"Hepatology"},{"issue":"8","key":"895_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhepr.2023.100792","volume":"5","author":"K Liu","year":"2023","unstructured":"Liu K, et al. Vessels that encapsulate tumour clusters vascular pattern in hepatocellular carcinoma. JHEP Rep. 2023;5(8): 100792.","journal-title":"JHEP Rep"},{"issue":"6","key":"895_CR11","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1002\/jmri.28488","volume":"57","author":"FM Chen","year":"2023","unstructured":"Chen FM, et al. Nomogram Estimating Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma From Preoperative Gadoxetate Disodium-Enhanced MRI. J Magn Reson Imaging. 2023;57(6):1893\u2013905.","journal-title":"J Magn Reson Imaging"},{"key":"895_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al., Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma. J Magn Reson Imaging, 2023.","DOI":"10.1002\/jmri.29064"},{"issue":"2","key":"895_CR13","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s00330-021-08250-9","volume":"32","author":"Y Yu","year":"2022","unstructured":"Yu Y, et al. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma. Eur Radiol. 2022;32(2):959\u201370.","journal-title":"Eur Radiol"},{"key":"895_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.triboint.2023.108410","volume":"183","author":"MH Esfe","year":"2023","unstructured":"Esfe MH, et al. Optimization of density and coefficient of thermal expansion of MWCNT in thermal oil nanofluid and modeling using MLP and response surface methodology. Tribol Int. 2023;183: 108410.","journal-title":"Tribol Int"},{"issue":"7","key":"895_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.arabjc.2023.104801","volume":"16","author":"A Sumayli","year":"2023","unstructured":"Sumayli A, Alshahrani SM. Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR). Arab J Chem. 2023;16(7): 104801.","journal-title":"Arab J Chem"},{"issue":"7","key":"895_CR16","doi-asserted-by":"publisher","first-page":"8999","DOI":"10.1016\/j.eswa.2011.01.118","volume":"38","author":"A Rohani","year":"2011","unstructured":"Rohani A, Abbaspour-Fard MH, Abdolahpour S. Prediction of tractor repair and maintenance costs using Artificial Neural Network. Expert Syst Appl. 2011;38(7):8999\u20139007.","journal-title":"Expert Syst Appl"},{"key":"895_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120389","volume":"228","author":"R Zhang","year":"2023","unstructured":"Zhang R, et al. MLP-based classification of COVID-19 and skin diseases. Expert Syst Appl. 2023;228: 120389.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"895_CR18","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s00330-022-08992-0","volume":"33","author":"W Zheng","year":"2023","unstructured":"Zheng W, et al. Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis. Eur Radiol. 2023;33(1):633\u201344.","journal-title":"Eur Radiol"},{"issue":"6","key":"895_CR19","doi-asserted-by":"publisher","first-page":"2830","DOI":"10.1007\/s00330-018-5932-y","volume":"29","author":"M Bonatti","year":"2019","unstructured":"Bonatti M, et al. Ascites relative enhancement during hepatobiliary phase after Gd-BOPTA administration: a new promising tool for characterising abdominal free fluid of unknown origin. Eur Radiol. 2019;29(6):2830\u20136.","journal-title":"Eur Radiol"},{"issue":"8","key":"895_CR20","doi-asserted-by":"publisher","first-page":"5840","DOI":"10.1007\/s00330-021-07702-6","volume":"31","author":"C Liu","year":"2021","unstructured":"Liu C, et al. Gadobenate dimeglumine-enhanced biliary imaging from the hepatobiliary phase can predict progression in patients with liver cirrhosis. Eur Radiol. 2021;31(8):5840\u201350.","journal-title":"Eur Radiol"},{"issue":"9","key":"895_CR21","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.25055","volume":"4","author":"WP Lin","year":"2021","unstructured":"Lin WP, et al. Development and Validation of a Model Including Distinct Vascular Patterns to Estimate Survival in Hepatocellular Carcinoma. JAMA Netw Open. 2021;4(9): e2125055.","journal-title":"JAMA Netw Open"},{"key":"895_CR22","doi-asserted-by":"publisher","first-page":"107327482211028","DOI":"10.1177\/10732748221102820","volume":"29","author":"R Guan","year":"2022","unstructured":"Guan R, et al. Development and Validation of a Novel Nomogram for Predicting Vessels that Encapsulate Tumor Cluster in Hepatocellular Carcinoma. Cancer Control. 2022;29:10732748221102820.","journal-title":"Cancer Control"},{"issue":"3","key":"895_CR23","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1148\/radiol.2018181494","volume":"289","author":"V Chernyak","year":"2018","unstructured":"Chernyak V, et al. Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology. 2018;289(3):816\u201330.","journal-title":"Radiology"},{"issue":"11","key":"895_CR24","doi-asserted-by":"publisher","first-page":"8615","DOI":"10.1007\/s00330-021-07941-7","volume":"31","author":"M Tang","year":"2021","unstructured":"Tang M, et al. Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI. Eur Radiol. 2021;31(11):8615\u201327.","journal-title":"Eur Radiol"},{"issue":"3","key":"895_CR25","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.jhep.2017.04.024","volume":"67","author":"S Lee","year":"2017","unstructured":"Lee S, et al. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol. 2017;67(3):526\u201334.","journal-title":"J Hepatol"},{"key":"895_CR26","doi-asserted-by":"publisher","first-page":"9674565","DOI":"10.1155\/2018\/9674565","volume":"2018","author":"M Huang","year":"2018","unstructured":"Huang M, et al. Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Preoperative Gd-EOB-DTPA-Dynamic Enhanced MRI and Histopathological Correlation. Contrast Media Mol Imaging. 2018;2018:9674565.","journal-title":"Contrast Media Mol Imaging"},{"issue":"10","key":"895_CR27","doi-asserted-by":"publisher","first-page":"6702","DOI":"10.1007\/s00330-022-09065-y","volume":"32","author":"C Zhou","year":"2022","unstructured":"Zhou C, et al. Histopathological components correlated with MRI features and prognosis in combined hepatocellular carcinoma-cholangiocarcinoma. Eur Radiol. 2022;32(10):6702\u201311.","journal-title":"Eur Radiol"},{"issue":"10","key":"895_CR28","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1111\/j.1872-034X.2010.00703.x","volume":"40","author":"Y Kawamura","year":"2010","unstructured":"Kawamura Y, et al. New classification of dynamic computed tomography images predictive of malignant characteristics of hepatocellular carcinoma. Hepatol Res. 2010;40(10):1006\u201314.","journal-title":"Hepatol Res"},{"issue":"5","key":"895_CR29","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1002\/hep.32494","volume":"76","author":"J Rimola","year":"2022","unstructured":"Rimola J, et al. Reliability of extracellular contrast versus gadoxetic acid in assessing small liver lesions using liver imaging reporting and data system vol 2018 and European association for the study of the liver criteria. Hepatology. 2022;76(5):1318\u201328.","journal-title":"Hepatology"},{"issue":"10","key":"895_CR30","doi-asserted-by":"publisher","first-page":"5327","DOI":"10.1007\/s00330-020-06895-6","volume":"30","author":"L Zhang","year":"2020","unstructured":"Zhang L, et al. Prediction of HCC microvascular invasion with gadobenate-enhanced MRI: correlation with pathology. Eur Radiol. 2020;30(10):5327\u201336.","journal-title":"Eur Radiol"},{"issue":"6","key":"895_CR31","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1002\/jmri.23940","volume":"37","author":"YS Choi","year":"2013","unstructured":"Choi YS, et al. Histological characteristics of small hepatocellular carcinomas showing atypical enhancement patterns on gadoxetic acid-enhanced MR imaging. J Magn Reson Imaging. 2013;37(6):1384\u201391.","journal-title":"J Magn Reson Imaging"},{"issue":"5","key":"895_CR32","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1111\/liv.12130","volume":"33","author":"K Takayasu","year":"2013","unstructured":"Takayasu K, et al. Clinical implication of hypovascular hepatocellular carcinoma studied in 4,474 patients with solitary tumour equal or less than 3 cm. Liver Int. 2013;33(5):762\u201370.","journal-title":"Liver Int"},{"issue":"10","key":"895_CR33","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1016\/j.ejrad.2011.11.014","volume":"81","author":"MJ Kim","year":"2012","unstructured":"Kim MJ, et al. Imaging features of small hepatocellular carcinomas with microvascular invasion on gadoxetic acid-enhanced MR imaging. Eur J Radiol. 2012;81(10):2507\u201312.","journal-title":"Eur J Radiol"},{"issue":"5","key":"895_CR34","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1159\/000494099","volume":"8","author":"L Yang","year":"2019","unstructured":"Yang L, et al. A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Liver Cancer. 2019;8(5):373\u201386.","journal-title":"Liver Cancer"},{"issue":"3","key":"895_CR35","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1002\/jmri.22876","volume":"35","author":"KA Kim","year":"2012","unstructured":"Kim KA, et al. Prediction of microvascular invasion of hepatocellular carcinoma: usefulness of peritumoral hypointensity seen on gadoxetate disodium-enhanced hepatobiliary phase images. J Magn Reson Imaging. 2012;35(3):629\u201334.","journal-title":"J Magn Reson Imaging"},{"issue":"16","key":"895_CR36","doi-asserted-by":"publisher","first-page":"8761","DOI":"10.1021\/acs.jmedchem.9b01101","volume":"63","author":"R Rodr\u00edguez-P\u00e9rez","year":"2020","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J. Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. J Med Chem. 2020;63(16):8761\u201377.","journal-title":"J Med Chem"},{"key":"895_CR37","doi-asserted-by":"crossref","unstructured":"Chen, H., et al., Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study. Abdom Radiol (NY), 2024.","DOI":"10.1007\/s00261-024-04283-y"},{"issue":"3","key":"895_CR38","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1053\/j.gastro.2009.06.003","volume":"137","author":"S Roayaie","year":"2009","unstructured":"Roayaie S, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology. 2009;137(3):850\u20135.","journal-title":"Gastroenterology"},{"issue":"1","key":"895_CR39","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1002\/jmri.28745","volume":"59","author":"X Dong","year":"2024","unstructured":"Dong X, et al. Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma. J Magn Reson Imaging. 2024;59(1):108\u201319.","journal-title":"J Magn Reson Imaging"},{"issue":"3","key":"895_CR40","doi-asserted-by":"publisher","first-page":"857","DOI":"10.4251\/wjgo.v16.i3.857","volume":"16","author":"C Zhang","year":"2024","unstructured":"Zhang C, et al. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol. 2024;16(3):857\u201374.","journal-title":"World J Gastrointest Oncol"},{"key":"895_CR41","doi-asserted-by":"crossref","unstructured":"Khan, R.U., et al., Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network. Diagnostics (Basel), 2022. 12(10).","DOI":"10.3390\/diagnostics12102539"},{"issue":"1","key":"895_CR42","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, et al. From Local Explanations to Global Understanding with Explainable AI for Trees. Nat Mach Intell. 2020;2(1):56\u201367.","journal-title":"Nat Mach Intell"}],"container-title":["Cancer Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00895-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40644-025-00895-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00895-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T10:57:30Z","timestamp":1751885850000},"score":1,"resource":{"primary":{"URL":"https:\/\/cancerimagingjournal.biomedcentral.com\/articles\/10.1186\/s40644-025-00895-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["895"],"URL":"https:\/\/doi.org\/10.1186\/s40644-025-00895-9","relation":{},"ISSN":["1470-7330"],"issn-type":[{"value":"1470-7330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,7]]},"assertion":[{"value":"14 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 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 studies involving human participants were reviewed and approved by The Eastern Hepatobiliary Surgery Hospital, the Third Affifiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China and Tongji Hospital, School of Medicine, Tongji University, Shanghai, China. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"87"}}