Multi-parametric MRI combined with radiomics for the diagnosis and grading of endometrial fibrosis

Background Accurate evaluation of endometrial fibrosis can help clinicians schedule individual treatment. This study aims to explore the application value of multi-parametric MRI (MP-MRI) combined with radiomics in the diagnosis and grading of endometrial fibrosis, aiming to construct models that can effectively distinguish endometrial fibrosis and compare the diagnostic performance of radiomics models established by different machine learning algorithms. Methods A total of 74 patients with severe endometrial fibrosis(SEF), 41 patients with mild to moderate fibrosis (MMEF)confirmed by hysteroscopy, and 40 healthy women of reproductive age were prospectively enrolled. All participants underwent T2 and DWI sequence scans during the periovulatory period. By freely delineating the volume of interest (VOI) of the endometrium in three subgroups, radiomic features were extracted and selected. Two feature selection methods and four machine learning classifiers were combined in pairs to establish five prediction models [model ₁ (T2 + ADC + clinical data), model ₂ (T2 + ADC), model ₃ (T2), model ₄ (ADC), and model ₅ (clinical data)], resulting in a total of 40 classification models. The predictive performance of all models was evaluated using the area under the curve (AUC), F1 score, and accuracy (ACC). Results Among the 40 classification models, the "UFS-LR" model, which combined unsupervised feature selection (UFS) with the logistic regression (LR) classifier, performed the best, with an average AUC of 0.92 on the test set. Among the five models constructed via UFS-LR, model ₁ exhibited the best performance, with average AUC, F1 score, and ACC values of 0.92, 0.79, and 0.81, respectively. The T2-related models had higher average AUC values than model ₄ and model ₅ did, especially in the MMEF and SEF groups. Among the optimal features selected from different models, T2-related features accounted for the largest number and had the highest weight. Conclusions Machine learning-based MP-MRI radiomics analysis exhibited excellent performance in grading endometrial fibrosis and has great potential for providing robust support for clinical diagnosis and treatment.