Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer.

Journal Article (Journal Article)

PURPOSE: We aimed to develop a noninvasive artificial intelligence (AI) model to diagnose signet-ring cell carcinoma (SRCC) of gastric cancer (GC) and identify patients with SRCC who could benefit from postoperative chemotherapy based on preoperative contrast-enhanced computed tomography (CT). METHODS: A total of 855 GC patients with 855 single GCs were included, of which 249 patients were diagnosed as SRCC by histopathologic examinations. The AI model was generated with clinical, handcrafted radiomic, and deep learning features. Model diagnostic performance was measured by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, while predictive performance was measured by Kaplan-Meier curves. RESULTS: In the test cohort (n = 257), the AUC, sensitivity, and specificity of our AI model for diagnosing SRCC were 0.786 (95% CI: 0.721-0.845), 77.3%, and 69.2%, respectively. For the entire cohort, patients with AI-predicted high risk had a significantly shorter median OS compared with those with low risk (median overall survival [OS], 38.8 vs. 64.2 months, p = 0.009). Importantly, in pathologically confirmed advanced SRCC patients, AI-predicted high-risk status was indicative of a shorter overall survival (median overall survival [OS], 31.0 vs. 54.4 months, p = 0.036) and marked chemotherapy resistance, whereas AI-predicted low-risk status had substantial chemotherapy benefit (median OS [without vs. with chemotherapy], 26.0 vs. not reached, p = 0.013). CONCLUSIONS: The CT-based AI model demonstrated good performance for diagnosing SRCC, stratifying patient prognosis, and predicting chemotherapy responses. Advanced SRCC patients with AI-predicted low-risk status may benefit substantially from adjuvant chemotherapy.

Full Text

Duke Authors

Cited Authors

  • Li, C; Qin, Y; Zhang, W-H; Jiang, H; Song, B; Bashir, MR; Xu, H; Duan, T; Fang, M; Zhong, L; Meng, L; Dong, D; Hu, Z; Tian, J; Hu, J-K

Published Date

  • March 2022

Published In

Volume / Issue

  • 49 / 3

Start / End Page

  • 1535 - 1546

PubMed ID

  • 35032039

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1002/mp.15437


  • eng

Conference Location

  • United States