Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.

Journal: Radiology. Artificial intelligence
Published Date: Mar 1, 2024

Abstract

Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic information. Materials and Methods In this retrospective study, the WISDOM model was built using MRI (T2-weighted and diffusion-weighted imaging) and patient-level pathologic information (the number of postoperatively confirmed metastatic LNs and resected LNs) based on the data of patients with RC between January 2016 and November 2017. The incremental value of the model in assisting radiologists was investigated. The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort ( = 589) and internal test cohort ( = 146) from center 1 and two external test cohorts (cohort 1: 117; cohort 2: 162) from centers 2 and 3. The WISDOM model yielded an overall AUC of 0.81 and C index of 0.765, significantly outperforming junior radiologists (AUC = 0.69, < .001; C index = 0.689, < .001) and performing comparably with senior radiologists (AUC = 0.79, = .21; C index = 0.788, = .22). Moreover, the model significantly improved the performance of junior radiologists (AUC = 0.80, < .001; C index = 0.798, < .001) and senior radiologists (AUC = 0.88, < .001; C index = 0.869, < .001). Conclusion This study demonstrates the potential of WISDOM as a useful LN diagnosis method using routine rectal MRI data. The improved radiologist performance observed with model assistance highlights the potential clinical utility of WISDOM in practice. MR Imaging, Abdomen/GI, Rectum, Computer Applications-Detection/Diagnosis . Published under a CC BY 4.0 license.

Authors

  • Wei Xia
    Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Dandan Li
    School of Medicine, Yangtze University, Jingzhou 434000, China.
  • Wenguang He
    From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).
  • Perry J Pickhardt
    University of Wisconsin Medical School, Department of Radiology, Madison, Wisconsin, United States.
  • Junming Jian
    University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Junjie Zhang
    Department of Immunology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China.
  • Ruirui Song
    Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.
  • Tong Tong
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Xiaotang Yang
    Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China. yangxt210@126.com.
  • Xin Gao
    Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
  • Yanfen Cui
    Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.

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