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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1532248
This article is part of the Research Topic Harnessing Big Data for Precision Medicine: Revolutionizing Diagnosis and Treatment Strategies View all 17 articles

Interpretable Machine Learning and Radiomics in Hip MRI Diagnostics: Comparing ONFH and OA Predictions to Experts

Provisionally accepted
Tariq Alkhatatbeh Tariq Alkhatatbeh 1Ahmad Alkhatatbeh Ahmad Alkhatatbeh 2Qin Guo Qin Guo 1Jiechen Chen Jiechen Chen 2Jidong Song Jidong Song 3Xingru Qini Xingru Qini 4Wang Wei Wang Wei 1*
  • 1 Comprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China
  • 2 Department of Orthopedics, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, China
  • 3 Orthopedic Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China
  • 4 Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi’an, Shaanxi Province, China

The final, formatted version of the article will be published soon.

    Purpose: 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.Methods: A sum of 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 a thorough selection procedure ofthoroughly selecting these features, various machine learning models have been constructed. The eEvaluation was carried out using receiver operating characteristic (ROC) curves. Then NaiveBayes (NB) was selected for the establishment of theto establish our final Radiomics-model 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.Results: The amount of handcrafted radiomics features was 1197 before processing; after the final selection, only 12 key features have beenwere 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, tThe 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.We have successfully constructed and evaluated an interpretable radiomics-based machine learning model that could distinguish between OA and ONFH. This method has the ability to aid both junior and senior medical professionals to precisely diagnose and take prompt treatment measures.

    Keywords: Radiomics, machine learning, Osteonecrosis, Osteoarthritis, Hip

    Received: 21 Nov 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Alkhatatbeh, Alkhatatbeh, Guo, Chen, Song, Qini and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor 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.

    * Correspondence: Wang Wei, Comprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.