AI Models for Gastric Cancer Diagnosis and Prognosis from Digital Pathology Images
Two deep learning models were developed for gastric cancer: GastroMIL for diagnosis (accuracy 0.920 in external validation, comparable to expert pathologists) and MIL-GC for overall survival prediction (C-index 0.657 externally). The prognostic model's risk score was an independent predictor of survival in multivariable analysis. The study demonstrates how AI-based digital pathology can simultaneously address diagnostic accuracy and prognostic stratification in gastric cancer.
The original study
Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.
- Authors
- Huang B, Tian S, Zhan N, Ma J, Huang Z, Zhang C, et al.
- Journal
- EBioMedicine
- Type
- Journal Article, Multicenter Study
- PMID
- 34678610
Original abstract
BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images. METHODS: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC. FINDINGS: The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/). INTERPRETATION: Our study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients. FUNDING: Not applicable.