Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images

Abstract

Background: Human evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), which is urgently needed to determine treatment and follow-up strategies for colon cancer. We aimed to develop an accurate histopathological feature for LNM in colon cancer. Methods: We developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Diseases and the pathology archive at the University Medical Center Mannheim in German. This model was applied to whole-slide pathological images of colon cancer patients from The Cancer Genome Atlas (TCGA). The predictive value of the peri-tumoral stroma (PTS) score for LNM was assessed. Findings: A total of 164 patients with stage I, II, and III colon cancer from TCGA were analyzed. The mean PTS score was 0.380 (±SD = 0.285), and significantly higher PTS scores were observed in patients in the LNM-positive group than those in the LNM-negative group (P < 0.001). In the univariate analyses, the PTS scores for the LNM-positive group were significantly higher than the scores for LNM-negative group (P < 0.001). Further, the PTS scores in lymphatic invasion and any one of perineural, lymphatic, or venous invasion were significantly increased in the LNM-positive group (P < 0.001 and P < 0.001). Interpretation: We established the PTS score, a simplified reproducible parameter, for predicting LNM in colon cancer using computer-based analysis that could be used to guide treatment decisions. These findings warrant further confirmation through large prospective clinical trials. Funding Statement: This research was supported by the Basic Science Research Program of National Research Foundation of Korea (NRF), which is funded by the Korean Ministry of Science, ICT and Future Planning (grant number: NRF-2019R1C1C1003524). Declaration of Interests: None of the authors have any conflicts of interest or financial ties to disclose. Ethics Approval Statement: This study was reviewed and approved by the Institutional Review Board of the Kyung Hee University Hospital at Gangdong (KHNMC IRB 2020-09-025). The need for informed consent was waived because all data used in this study were de-identified.