Sun Man, Wang Jianzhang, Xu Ping, Zhu Libo, Zou Gen, Chen Shuyi, Liu Yuanmeng, Zhang Xinmei
Department of Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ann Med. 2025 Dec;57(1):2534521. doi: 10.1080/07853890.2025.2534521. Epub 2025 Jul 25.
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.
外在性子宫腺肌病表现出异质性临床症状，疼痛较为常见。磁共振成像（MRI）特征与症状之间的关系尚不清楚。
评估MRI放射组学模型区分外在性子宫腺肌病症状异质性（疼痛、异常子宫出血（AUB）、不孕和无症状）的性能。
本回顾性分析纳入了405例经MRI诊断为外在性子宫腺肌病的患者（2020年1月至2022年7月），按7:3随机分为训练组和测试组。从MRI-T2图像中提取放射组学特征。采用随机森林算法选择不同症状的关键放射组学特征，并通过支持向量机算法建立放射组学模型。多变量逻辑回归评估临床特征。创建了一个联合放射组学-临床列线图用于症状分层。
405例患者共有496种临床症状。在训练组和测试组中，放射组学模型在区分疼痛、AUB、不孕和无症状时的曲线下面积（AUC）分别为0.73/0.72、0.82/0.76、0.84/0.80和0.80/0.71。多特征模型（放射组学+临床特征）表现更佳，列线图显示出良好的分层能力：训练组/测试组中区分疼痛、AUB、不孕和无症状时的AUC分别为0.78/0.78、0.87/0.85、0.89/0.88和0.84/0.81。
我们确定了外在性子宫腺肌病关键放射组学特征与临床症状之间的相关性。基于机器学习的MRI放射组学模型对外在性子宫腺肌病症状分层具有潜力，可能减少不必要的治疗。
