Li Chenrong, Tan Jing, Li Haiyan, Lei Ying, Yang Guang, Zhang Chengxiu, Song Yang, Wu Yunzhu, Bi Guoli, Bi Qiu
Medical school, Kunming University of Science and Technology, The First People's Hospital of Yunnan Province, Kunming, 650500, Yunnan, China.
Department of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University，Peking University Cancer Hospital Yunnan, Kunming, 650118, Yunnan, China.
Abdom Radiol (NY). 2025 Feb;50(2):995-1008. doi: 10.1007/s00261-024-04539-7. Epub 2024 Aug 25.
To explore the feasibility of multiparametric MRI-based habitat imaging for distinguishing uterine sarcoma (US) from atypical leiomyoma (ALM).
This retrospective study included the clinical and preoperative MRI data of 69 patients with US and 225 patients with ALM from three hospitals. At both the individual and cohort levels, the K-means and Gaussian mixture model (GMM) algorithms were utilized to perform habitat imaging on MR images, respectively. Specifically, T2-weighted images (T2WI) and contrast-enhanced T1-weighted images (CE-T1WI) were clustered to generate structural habitats, while apparent diffusion coefficient (ADC) maps and CE-T1WI were clustered to create functional habitats. Parameters of each habitat subregion were extracted to construct distinct habitat models. The integrated models were constructed by combining habitat and clinical independent predictors. Model performance was assessed using the area under the curve (AUC).
Abnormal vaginal bleeding, lactate dehydrogenase (LDH), and white blood cell (WBC) counts can serve as clinical independent predictors of US. The GMM-based functional habitat model at the cohort level had the highest mean AUC (0.766) in both the training and validation cohorts, followed by the GMM-based structural habitat model at the cohort level (AUC = 0.760). Within the integrated models, the K-means functional habitat model based on the cohort level achieved the highest mean AUC (0.905) in both the training and validation cohorts.
Habitat imaging based on multiparametric MRI has the potential to distinguish US from ALM. The combination of clinical independent predictors with the habitat models can effectively improve the performance.
探讨基于多参数磁共振成像（MRI）的栖息地成像用于区分子宫肉瘤（US）和非典型平滑肌瘤（ALM）的可行性。
这项回顾性研究纳入了来自三家医院的69例子宫肉瘤患者和225例非典型平滑肌瘤患者的临床及术前MRI数据。在个体和队列水平上，分别利用K均值和高斯混合模型（GMM）算法对MR图像进行栖息地成像。具体而言，对T2加权图像（T2WI）和对比增强T1加权图像（CE-T1WI）进行聚类以生成结构栖息地，而对表观扩散系数（ADC）图和CE-T1WI进行聚类以创建功能栖息地。提取每个栖息地子区域的参数以构建不同的栖息地模型。通过结合栖息地和临床独立预测因子构建综合模型。使用曲线下面积（AUC）评估模型性能。
异常阴道出血、乳酸脱氢酶（LDH）和白细胞（WBC）计数可作为子宫肉瘤的临床独立预测因子。队列水平上基于GMM的功能栖息地模型在训练和验证队列中的平均AUC最高（0.766），其次是队列水平上基于GMM的结构栖息地模型（AUC = 0.760）。在综合模型中，基于队列水平的K均值功能栖息地模型在训练和验证队列中的平均AUC最高（0.905）。
基于多参数MRI的栖息地成像有潜力区分子宫肉瘤和非典型平滑肌瘤。临床独立预测因子与栖息地模型相结合可有效提高性能。
