Radiomics-based diagnosis of patellar chondromalacia using sagittal T2-weighted images.

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This study aimed to explore and evaluate anovel method for diagnosing patellar chondromalacia using radiomic features from patellar sagittal T2-weighted images (T2WI). The experimental data included sagittal T2WI images of the patella from 40patients with patellar chondromalacia and 40healthy volunteers. The training set comprised 30cases of chondromalacia and 30healthy volunteers, while the test set included 10cases of each. Amachine learning algorithm was used to train the classification model, which was then evaluated using standard performance metrics. In the training set, the model achieved24 true negatives(TN), 18true positives(TP), 12false negatives(FN), and sixfalse positives(FP). Sensitivity, specificity, accuracy, and F1score for the training set were 0.6, 0.8, 0.7, and 0.667, respectively. The model achieved sixtrue negatives, eighttrue positives, twofalse negatives, and fourfalse positives in the test set. Sensitivity, specificity, accuracy, and F1score for the test set were 0.8, 0.6, 0.7, and 0.727, respectively. The radiomic analysis method based on patellar sagittal fat-suppressed T2WI images demonstrates good diagnostic capability for patellar bone marrow edema.