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<p>We evaluated the effectiveness of magnetic resonance imaging (MRI)-based subregional texture analysis (TA) models for classifying knee osteoarthritis (OA) severity grades by compartment. We identified 122 MR images of 121 patients with knee OA (mild-to-severe OA equivalent to Kellgren–Lawrence grades 2–4), comprising sagittal proton density-weighted imaging and axial fat-suppressed proton density-weighted imaging. The data were divided into OA severity groups by medial, lateral, and articulation between the patella and femoral trochlea (P-FT) compartments (three groups for the medial compartment and two for the lateral and P-FT compartments). After extracting 93 texture features and dimension reduction for each compartment and imaging, models were created using linear discriminant analysis, support vector machine with linear, radial basis function, sigmoid kernels, and random forest classifiers. Models underwent 100-time repeat nested cross validations. We applied our classification model to total knee OA severity. The models’ performance was modest for both compartments and total knee. The medial compartment showed better results than the lateral and patellofemoral compartments. Our MRI-based compartmental TA model can potentially differentiate between subregional OA severity grades. Further studies are needed to assess the feasibility of our subregional TA method and machine learning algorithms for classifying OA severity by compartment.</p>

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Using magnetic resonance imaging-based subregional texture analysis models to classify knee osteoarthritis severity by compartment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Keita Nagawa,
  • Yuki Hara,
  • Shinji Kakemoto,
  • Taira Shiratori,
  • Akane Kaizu,
  • Masahiro Koyama,
  • Saki Tsuchihashi,
  • Hirokazu Shimizu,
  • Kaiji Inoue,
  • Naoki Sugita,
  • Eito Kozawa

摘要

We evaluated the effectiveness of magnetic resonance imaging (MRI)-based subregional texture analysis (TA) models for classifying knee osteoarthritis (OA) severity grades by compartment. We identified 122 MR images of 121 patients with knee OA (mild-to-severe OA equivalent to Kellgren–Lawrence grades 2–4), comprising sagittal proton density-weighted imaging and axial fat-suppressed proton density-weighted imaging. The data were divided into OA severity groups by medial, lateral, and articulation between the patella and femoral trochlea (P-FT) compartments (three groups for the medial compartment and two for the lateral and P-FT compartments). After extracting 93 texture features and dimension reduction for each compartment and imaging, models were created using linear discriminant analysis, support vector machine with linear, radial basis function, sigmoid kernels, and random forest classifiers. Models underwent 100-time repeat nested cross validations. We applied our classification model to total knee OA severity. The models’ performance was modest for both compartments and total knee. The medial compartment showed better results than the lateral and patellofemoral compartments. Our MRI-based compartmental TA model can potentially differentiate between subregional OA severity grades. Further studies are needed to assess the feasibility of our subregional TA method and machine learning algorithms for classifying OA severity by compartment.