Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images - Kwak, Min Seob; Lee, Hun Hee; Yang, Jae Min; Cha, Jae Myung; Jeon, Jung Won; Yoon, Jin Young; Kim, Ha Il (Frontiers in Oncology, 2021)
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. Results: 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). Conclusion: 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.