MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images
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.
Authors
- Hong-Yi Gao (ORCID: https://orcid.org/0000-0001-7346-8788)
- Bingzhong Jing
- Xia Yang (ORCID: https://orcid.org/0000-0002-5388-0211)
- Rongzhen Luo (ORCID: https://orcid.org/0000-0002-6599-0370)
- Chaozhi Zheng (ORCID: https://orcid.org/0000-0001-6030-3933)
- Kang Xiao-ling
- Ronggang Li (ORCID: https://orcid.org/0000-0001-7126-0182)
- Xiaoming Ouyang (ORCID: https://orcid.org/0000-0002-6632-3190)
- Jiangyu Zhang (ORCID: https://orcid.org/0000-0003-1714-3684)
- Lili Liu (ORCID: https://orcid.org/0009-0000-3619-911X)
- Xuan Liu (ORCID: https://orcid.org/0000-0002-7966-4488)
- Haohua Chen (ORCID: https://orcid.org/0000-0001-6210-6415)
- Dongdong Wang (ORCID: https://orcid.org/0000-0003-2737-6748)
- Lijun Wei
- Chao Cao (ORCID: https://orcid.org/0009-0009-6844-519X)
- X.F. Steven Zheng (ORCID: https://orcid.org/0000-0003-0813-3599)
- Zihan Zhao (ORCID: https://orcid.org/0000-0002-5871-8440)
- Xinke Zhang (ORCID: https://orcid.org/0000-0002-3527-2140)
- Quanling Kong
- Mu Yan Cai
- Ma-Yan Huang
- Haoyu Liang
- Wan Zhao
Institutions
- Qingdao University (CN)
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- Qingdao Municipal Hospital (CN)
- Guangdong Province Women and Children Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Cell Reports Medicine
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1016/j.xcrm.2025.102099
- Citations
- 4
- Primary Topic
- Genetic factors in colorectal cancer
- Type
- article
- Field-Weighted Citation Impact
- 5.89
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University Cancer Center