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

Objective: This study aimed to explore and evaluate a novel method for diagnosing patellar chondromalacia using radiomic features from patellar sagittal T2-weighted images (T2WI).

Methods: The experimental data included sagittal T2WI images of the patella from 40 patients with patellar chondromalacia and 40 healthy volunteers. The training set comprised 30 cases of chondromalacia and 30 healthy volunteers, while the test set included 10 cases of each. A machine learning algorithm was used to train the classification model, which was then evaluated using standard performance metrics.

Results: In the training set, the model achieved 24 true negatives (TN), 18 true positives (TP), 12 false negatives (FN), and six false positives (FP). Sensitivity, specificity, accuracy, and F1 score for the training set were 0.6, 0.8, 0.7, and 0.667, respectively. The model achieved six true negatives, eight true positives, two false negatives, and four false positives in the test set. Sensitivity, specificity, accuracy, and F1 score for the test set were 0.8, 0.6, 0.7, and 0.727, respectively.

Conclusion: The radiomic analysis method based on patellar sagittal fat-suppressed T2WI images demonstrates good diagnostic capability for patellar bone marrow edema.