Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts
PurposeDistinguishing 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 betw...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1532248/full |
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| author | Tariq Alkhatatbeh Ahmad Alkhatatbeh Qin Guo Jiechen Chen Jidong Song Xingru Qin Wang Wei |
| author_facet | Tariq Alkhatatbeh Ahmad Alkhatatbeh Qin Guo Jiechen Chen Jidong Song Xingru Qin Wang Wei |
| author_sort | Tariq Alkhatatbeh |
| collection | DOAJ |
| description | PurposeDistinguishing 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.Methods140 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 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.ResultsThe 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.ConclusionWe 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. |
| format | Article |
| id | doaj-art-e1845c8fae9348929218724beb279779 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-e1845c8fae9348929218724beb2797792025-01-29T06:45:50ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011610.3389/fimmu.2025.15322481532248Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to expertsTariq Alkhatatbeh0Ahmad Alkhatatbeh1Qin Guo2Jiechen Chen3Jidong Song4Xingru Qin5Wang Wei6Comprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaDepartment of Orthopedics, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, ChinaComprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaDepartment of Orthopedics, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, ChinaOrthopedic Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaDepartment of Radiology, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaComprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaPurposeDistinguishing 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.Methods140 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 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.ResultsThe 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.ConclusionWe 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.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1532248/fullradiomicsmachine learningosteonecrosisosteoarthritiship |
| spellingShingle | Tariq Alkhatatbeh Ahmad Alkhatatbeh Qin Guo Jiechen Chen Jidong Song Xingru Qin Wang Wei Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts Frontiers in Immunology radiomics machine learning osteonecrosis osteoarthritis hip |
| title | Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts |
| title_full | Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts |
| title_fullStr | Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts |
| title_full_unstemmed | Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts |
| title_short | Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts |
| title_sort | interpretable machine learning and radiomics in hip mri diagnostics comparing onfh and oa predictions to experts |
| topic | radiomics machine learning osteonecrosis osteoarthritis hip |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1532248/full |
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