LAUSR

Objective To develop and evaluate a T 2 MR–based radiomics prediction model incorporating radiomics features and clinical parameters to predict the response to magnetic resonance–guided focused ultrasound surgery (MRgFUS) in patients with adenomyosis. Materials and methods Sixty-nine patients (mean age, 38.6 years; age range, 26–50 years) with adenomyosis treated by MRgFUS were reviewed and allocated to training ( n  = 48) and testing cohorts ( n  = 21). One thousand one hundred eighteen radiomics features were extracted from T 2 -weighted imaging before MRgFUS. The radiomics features’ dimension was reduced by Pearson correlation coefficient after normalization. Analysis of variance and logistical regression were used for feature selection by fivefold cross-validation in the training cohort, and the machine learning model was constructed for comparing the clinical model, radiomics model, and radiomics-clinical model which combined survived radiomics features and clinical parameters. The discrimination result of the model was obtained by bootstrap; receiver operating characteristic curve, area under the curve (AUC), and decision curve analyses were performed to illustrate the model performance in both the training and testing cohorts. Results Good response was achieved in 47 patients (68.1%) and failed in 22 patients (38.9%). The radiomics model comprised four selected features and demonstrated a degree of prediction capability of patients’ poor response to MRgFUS treatment. The radiomics-clinical model showed good discrimination, with an AUC of 0.81 (95% confidence interval, 0.592–0.975) in the testing cohort. The decision curve analysis also showed favorable performance of the radiomics-clinical model. Conclusions A prediction model composed of T2WI-based radiomics features and clinical parameters could be applied to guide the radiologist to evaluate MRgFUS for patients with adenomyosis who will achieve good response. Key Points • Magnetic resonance imaging–guided focused ultrasound surgery represents an alternative treatment for adenomyosis, but nearly one third of patients remain symptomatic 6 months after MRgFUS. • Combining four radiomics features of T 2 -weighted MRI with eight clinical features further improves prediction of poor responders to MR-guided focused ultrasound treatment of uterine adenomyosis (AUC = 0.81 in the testing cohort). • The radiomics model based on T 2 -weighted imaging combined with clinical parameters can help predict which patients are likely to have a good response to MRgFUS for adenomyosis.