Prediction of lymph node metastasis in primary gastric cancer from pathological images and clinical data by multimodal multiscale deep learning

Abstract

Background:Gastric cancer is the third most common cause of cancer-related death. Accurate preoperative prediction of lymph node metastasis (LNM) in primary gastric cancer strongly influences the choice of surgical approach and the prognosis of gastric cancer patients. Objective:To develop and validate a deep learning-based model to analyze routine histological slides of patients with primary gastric cancer as well as clinical data to predict the occurrence of LNM preoperatively. Patients and Methods:Radical surgery slides from 309 patients and biopsy slides from 157 patients were collected from Fujian Cancer Hospital, along with radical surgery slides from 306 patients from The Cancer Genome Atlas (TCGA). Clinical data, including age, gender, lauren classification, and tumor location, were collected. These datasets were used to develop and validate a deep learning-based model. Results:Five models were trained via cross-validation, with a mean area under the receiver operating characteristic curve (AUC) (standard deviation [SD]) of 0.877 (0.048) achieved. There was a significant difference in scores between both classes (LNM positive [N+] and LNM negative [N0]) ( p<0.001). we validated the performance of the model on biopsy slides and achieved a mean AUC (SD) of 0.725 (0.020). In the analysis of clinical data, the lauren classification was demonstrated to be an independent risk factor for predicting LNM. Conclusion:Our study confirmed that deep learning-based image analysis could preoperatively predict LNM in patients with primary gastric cancer, combining histological slides at different magnification scales and relevant clinical data, showing superiority over individual modality prediction.