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This retrospective study develops and validates an interpretable machine learning model combining multisequence MRI radiomics with clinical features to preoperatively predict pathological grades of hepatocellular carcinoma. MRI and clinical data from 125 confirmed HCC patients were analyzed with logistic regression, feature selection, multiple candidate predictive models, and ROC/AUC evaluation. Decision curve analysis and SHapley Additive exPlanations interpreted key contributing features. Results show the combined radiomics-clinical model achieved the best predictive performance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/interpretable-model-based-on-multisequence-magnetic-resonance-imaging-radiomics-for-predicting-the-pathological-grades-of-hepatocellular-carcinomas/406814/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/interpretable-model-based-on-multisequence-magnetic-resonance-imaging-radiomics-for-predicting-the-pathological-grades-of-hepatocellular-carcinomas/406814.png","ImageObject",300,407,{"name":92,"@type":93},"Alexandar","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is explainability important for machine learning models in clinical practice?","Question",{"text":112,"@type":113},"The study notes that complexity and lack of explainability make AI/ML models difficult to apply directly in clinical workflows, motivating an interpretable approach.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and patient design were used to build the prediction model?",{"text":117,"@type":113},"MRI and clinical data from 125 HCC patients with postoperative pathological confirmation were retrospectively analyzed and split into training and validation groups in a 7:3 ratio.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best and how were key features interpreted?",{"text":121,"@type":113},"The combined radiomics-clinical (RC) model demonstrated the best performance. 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We developed and validated an interpretable ML model based on magnetic resonance imaging (MRI) radiomics and clinical features for the preoperative prediction of the pathological grades of hepatocellular carcinomas (HCCs) . This model will help clinicians better understand the situation and develop personalized treatment plans.  \nAIM  \nTo develop and validate an interpretable ML model for preoperative pathological grade prediction in HCC patients via a combination of multisequence MRI radiomics and clinical features.  \nMETHODS  \nMRI and clinical data derived from 125 patients with HCCs confirmed by postoperative pathological examinations were retrospectively analyzed. The patients were randomly split into training and validation groups (7:3 ratio) . Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors. The tumor lesions observed on axial fat-  \n WJR  [https://www.wjgnet.com](https://www.wjgnet.com) 1 December 28, 2025  Volume 17  Issue 12   \nShi Y et al. MRI radiomics model to predict the pathological grade of HCC  \nsuppressed T2-weighted imaging (FS-T2WI), arterial phase (AP), and portal venous phase (PVP) images were delineated in a slice-by-slice manner using 3D-slicer to generate volumetric regions of interest, and radiomic features were extracted. Interclass correlation coefficients were calculated, and least absolute selection and shrinkage operator regression were conducted for feature selection purposes. Six predictive models were subsequently developed for pathological grade prediction: FS-T2WI, AP, PVP, integrated radiomics, clinical, and combined radiomics-clinical (RC) models. The effectiveness of these models w","cbCaitVw3qoTqtmi","https://ap.wps.com/l/cbCaitVw3qoTqtmi","pdf",2865537,15,"English","# Abstract\n## Background\n## Aim\n## Methods\n## Results\n## Conclusion","[{\"question\":\"Why is explainability important for machine learning models in clinical practice?\",\"answer\":\"The study notes that complexity and lack of explainability make AI/ML models difficult to apply directly in clinical workflows, motivating an interpretable approach.\"},{\"question\":\"What data and patient design were used to build the prediction model?\",\"answer\":\"MRI and clinical data from 125 HCC patients with postoperative pathological confirmation were retrospectively analyzed and split into training and validation groups in a 7:3 ratio.\"},{\"question\":\"Which model performed best and how were key features interpreted?\",\"answer\":\"The combined radiomics-clinical (RC) model demonstrated the best performance. SHapley Additive exPlanations identified the most influential feature contributions and their direction of effect.\"}]","Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas | PDF",1790568098,38]