[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122195-en":3,"doc-seo-122195-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122195,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Interpretable machine learning and radiomics in hip MRI diagnostics - comparing ONFH and OA predictions to experts","Distinguishing osteonecrosis of the femoral head (ONFH) from osteoarthritis (OA) remains subjective and varies across clinicians with different expertise. This study builds and evaluates several radiomics-based machine learning models using hip MRI to differentiate ONFH and OA and compares their performance with medical experts. Radiomics features were extracted from retrospectively collected MRI scans, followed by feature selection and ROC-based evaluation. The final Naive Bayes radiomics model achieved high AUC with strong sensitivity and specificity, supporting clinically beneficial decision support.","TYPE Original Research PUBLISHED 29 January 2025  \nDOI 10.3389/fimmu.2025.1532248  \nOPEN ACCESS  \nEDITED BY  \nLushan Xiao,  \nSouthern Medical University, China  \nREVIEWED BY  \nZekun Jiang,  \nSichuan University, China Eros Montin,  \nNew York University, United States Aobo Zhang,  \nPeking University Hospital of Stomatology, China  \n*CORRESPONDENCE  \nWang Wei  \n [dr.wangwei@xjtu.edu.cn](dr.wangwei@xjtu.edu.cn)  \nRECEIVED 21 November 2024  \nACCEPTED 13 January 2025  \nPUBLISHED 29 January 2025  \nCITATION  \nAlkhatatbeh T, Alkhatatbeh A, Guo Q, Chen J, Song J, Qin X and Wei W (2025) Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.  \nFront. Immunol. 16:1532248 .  \ndoi: 10.3389/fimmu.2025.1532248  \nCOPYRIGHT  \n© 2025 Alkhatatbeh, Alkhatatbeh, Guo, Chen, Song, Qin and Wei. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nInterpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFHand OA predictions to experts  \nTariq Alkhatatbeh 1, Ahmad Alkhatatbeh 2, Qin Guo 1,  \nJiechen Chen 2, Jidong Song 3, Xingru Qin 4 and Wang Wei 1*  \n1Comprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China, 2 Department of Orthopedics, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China, 3Orthopedic Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China, 4 Department of Radiology, the Second Affiliated Hospital of Xi ’an Jiaotong University, Xi’an, Shaanxi, China  \nPurpose: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based machine learning models using MRI to differentiate between those two disorders and compare their efficacies to those of medical experts.  \nMethods: 140 MRI scans were retrospectively collected from the electronic medical records. They were split into training and testing sets in a 7:3 ratio. Handcrafted radiomics features were harvested following the careful manual segmentation of the regions of interest (ROI) . After thoroughly selecting these features, various machine learning models have been constructed. The evaluation was carried out using receiver operating characteristic (ROC) curves. Then NaiveBayes (NB) was selected to establish our final Radiomicsmodel as it performed the best. Three users with different expertise and backgrounds diagnosed and labeled the dataset into either OA or ONFH. Their results have been compared to our Radiomics-model.  \nResults: The amount of handcrafted radiomics features was 1197 before processing; after the final selection, only 12 key features were retained and used. User 1 had an AUC of 0 . 632 (95% CI 0 .4801-0.7843), User 2 recorded an AUC of 0.565 (95% CI 0.4102-0.7196); while User 3 was on top with an AUC of 0. 880 (95% CI 0 .7753-0. 9843) . On the other hand, the Radiomics model attained an AUC of 0.971 (95% CI 0.9298-1.0000); showing greater efficacy than all other users. It also demonstrated a sensitivity of 0 . 937 and a specificity of 0 .885. DCA (Decision Curve Analysis displayed that the radiomics-model had a greater clinical benefit in differentiating OA and ONFH.  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nConclusion: We have successfully constructed and evaluated an interpretable radiomics-based machine learning model that could distinguish between OA and","cbCaikHOLKqSTLnd","https://ap.wps.com/l/cbCaikHOLKqSTLnd","pdf",4738618,1,11,"English","en",105,"# Purpose\n# Methods\n# Results\n# Conclusion\n# Keywords\n# Introduction\n## MRI and clinical diagnostic challenges\n## Radiomics and machine learning in medical imaging","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study targets the subjective differentiation between osteonecrosis of the femoral head (ONFH) and osteoarthritis (OA) on hip MRI, where interpretation can vary by clinician expertise.\"},{\"question\":\"How were the radiomics features and models constructed?\",\"answer\":\"Radiomics features were extracted after manual segmentation of regions of interest, followed by feature selection and ROC-based evaluation across several machine learning models; Naive Bayes was selected as the final model.\"},{\"question\":\"How did the final radiomics model perform compared with experts?\",\"answer\":\"The radiomics model reached an AUC of 0.971 with high sensitivity (0.937) and specificity (0.885), outperforming the users’ AUC values.\"}]","Interpretable machine learning and radiomics in hip MRI diagnostics - comparing ONFH and OA predictions to experts | PDF",1785809289,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"interpretable-machine-learning-and-radiomics-in-hip-mri-diagnostics-comparing-onfh-and-oa-predictions-to-experts","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interpretable-machine-learning-and-radiomics-in-hip-mri-diagnostics-comparing-onfh-and-oa-predictions-to-experts/122195/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the study address?","Question",{"text":75,"@type":76},"The study targets the subjective differentiation between osteonecrosis of the femoral head (ONFH) and osteoarthritis (OA) on hip MRI, where interpretation can vary by clinician expertise.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the radiomics features and models constructed?",{"text":80,"@type":76},"Radiomics features were extracted after manual segmentation of regions of interest, followed by feature selection and ROC-based evaluation across several machine learning models; 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