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

Journal: Cell reports. Medicine
Published Date: Apr 29, 2025

Abstract

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

  • Li-Li Liu
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Bing-Zhong Jing
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Xuan Liu
    Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA.
  • Rong-Gang Li
    Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China.
  • Zhao Wan
    Department of Pathology, Zhuhai Maternal and Child Health Care Hospital, Zhuhai 519000, China.
  • Jiang-Yu Zhang
    Department of Pathology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, China.
  • Xiao-Ming Ouyang
    Department of Pathology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China.
  • Qing-Nuan Kong
    Department of Pathology, Qingdao Municipal Hospital, Qingdao 266071, China.
  • Xiao-Ling Kang
    Department of Pathology, Guangdong Women and Children Hospital, Guangzhou 511400, China.
  • Dong-Dong Wang
    Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China. 13852029591@163.com.
  • Hao-Hua Chen
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Zi-Han Zhao
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Hao-Yu Liang
    Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
  • Ma-Yan Huang
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Cheng-You Zheng
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Xia Yang
    From the Department of Integrative Biology and Physiology, University of California, Los Angeles (Y.Z., Q.M., X.Y.); and Target Sciences Computational Biology (US), GSK, King of Prussia, PA (J.C., J.M.F., D.K.R.). xyang123@ucla.edu deepak.k.rajpal@gsk.com.
  • Xue-Yi Zheng
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Xin-Ke Zhang
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Li-Jun Wei
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Chao Cao
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Hong-Yi Gao
    Department of Pathology, Guangdong Women and Children Hospital, Guangzhou 511400, China. Electronic address: 1522712335@qq.com.
  • Rong-Zhen Luo
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China. Electronic address: luorzh@sysucc.org.cn.
  • Mu-Yan Cai
    Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, PR China.

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