MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images

作者：Lili Liu, Bingzhong Jing, Xuan Liu, Ronggang Li, Wan Zhao, Jiangyu Zhang, Xiaoming Ouyang, Quanling Kong, Kang Xiao-ling, Dongdong Wang, Haohua Chen, Zihan Zhao, Haoyu Liang, Ma-Yan Huang, Cheng-You Zheng, Xia Yang, Xue-Yi Zheng, Xinke Zhang, Lijun Wei, Chao Cao, Hong-Yi Gao, Rongzhen Luo, Mu Yan Cai · 发表于：Cell Reports Medicine · 年份：2025 · DOI：10.1016/j.xcrm.2025.102099 · 被引用次数：6 · 研究领域：Genetic factors in colorectal cancer、Cancer-related molecular mechanisms research、Colorectal and Anal Carcinomas

Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying immunotherapy candidates. An affordable and accessible tool is urgently needed to determine MMR status in EC patients. We introduce MMRNet, a deep convolutional neural network designed to predict MMR-deficient EC from whole-slide images stained with hematoxylin and eosin. MMRNet demonstrates strong performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.897, with a sensitivity of 0.628 and a specificity of 0.949 in internal cross-validation. External validation using three additional datasets results in AUROCs of 0.790, 0.807, and 0.863. Employing a human-machine fusion approach notably improves diagnostic accuracy. MMRNet presents an effective method for identifying EC cases for confirmatory MMR testing and may assist in selecting candidates for immunotherapy.