Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis

• Published in: Annals of medicine (Helsinki) Vol. 57; no. 1; p. 2534521
• Main Authors: Sun, Man, Wang, Jianzhang, Xu, Ping, Zhu, Libo, Zou, Gen, Chen, Shuyi, Liu, Yuanmeng, Zhang, Xinmei
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.12.2025; Taylor & Francis Group
• Subjects: Adenomyosis; Adenomyosis - complications; Adenomyosis - diagnosis; Adenomyosis - diagnostic imaging; Adult; Female; heterogeneity; Humans; Magnetic Resonance Imaging - methods; Middle Aged; MRI; Nomograms; Pain - etiology; Pregnancy, Childbirth & Women’s Health; Radiomics; Retrospective Studies; Support Vector Machine; symptom; Uterine Hemorrhage - etiology; radiomics; symptom; MRI; Adenomyosis; heterogeneity
• ISSN: 0785-3890; 1365-2060; 1365-2060
• DOI: 10.1080/07853890.2025.2534521

Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear. To evaluate the performance of MRI radiomics model for differentiating symptom heterogeneity of extrinsic adenomyosis, pain, abnormal uterine bleeding (AUB), infertility, and no symptom. This retrospective analysis included 405 patients with MRI-diagnosed extrinsic adenomyosis (January 2020-July 2022), randomly split 7:3 into training and test cohorts. Radiomic features were extracted from MRI-T2 image. Random forest algorithm was used to select the key radiomics features of different symptoms and develop the radiomic model by support vector machine algorithm. Multivariable logistic regression assessed clinical characteristics. A combined radiomics-clinical nomogram was created for symptom stratification. In total 405 patients presented with 496 clinical symptoms. In the training and test cohorts, radiomics models achieved areas under the curve (AUCs) of 0.73/0.72 (pain), 0.82/0.76 (AUB), 0.84/0.80 (infertility), and 0.80/0.71 (no symptom). The multi-signature model (radiomic + clinical features) showed improved performance, with the nomogram demonstrating good stratification ability: AUCs of 0.78/0.78 (pain), 0.87/0.85 (AUB), 0.89/0.88 (infertility), and 0.84/0.81 (no symptom) in the training/test cohort. We identified the correlation between key radiomic features and clinical symptom of extrinsic adenomyosis. The machine learning-based MRI radiomics models have potential for symptom stratification of extrinsic adenomyosis and may potentially reduce unnecessary treatment.