Artificial intelligence in gastric cancer diagnosis, treatment and prognostic prediction: current application and future perspective

doi:10.19401/j.cnki.1007-3639.2025.05.008

Abstract

Abstract:

Gastric cancer remains one of the most prevalent and lethal malignancies worldwide, characterized by an insidious onset, challenges in early detection, and a poor prognosis in advanced stages. Conventional diagnostic approaches are often constrained by subjective interpretation and inherent limitations in accuracy and efficiency, rendering them insufficient to meet the demands of precision medicine. In recent years, the rapid advancement of artificial intelligence (AI), particularly deep learning (DL)-based techniques, has opened new avenues for the precise diagnosis and management of gastric cancer. Emerging evidence suggests that AI-assisted endoscopic systems significantly enhance lesion detection rates and diagnostic efficiency, while AI-driven radiomics models offer precise predictions of tumor invasion depth, lymph node involvement, and peritoneal metastasis. Additionally, AI-powered pathology analysis has markedly improved both diagnostic accuracy and efficiency. Moreover, integrative AI models leveraging multi-omics data have demonstrated great potential in predicting responses to chemotherapy and targeted therapies, as well as facilitating personalized prognostic assessments. However, despite these promising advancements, the clinical implementation of AI in gastric cancer remains hindered by challenges such as the lack of standardized datasets, limited model generalizability, and insufficient algorithm interpretability. This review systematically synthesized the latest advancements in AI applications for gastric cancer diagnosis, treatment response evaluation, and prognostic prediction. Furthermore, it critically examined key technical challenges in current AI methodologies and explored future directions in AI-driven precision medicine for gastric cancer. By addressing these challenges, we aimed to foster the widespread adoption and clinical translation of AI technologies, ultimately advancing precision oncology and improving patient outcomes.

Key words: Artificial intelligence, Gastric cancer, Deep learning, Diagnosis, Prognosis

CLC Number:

R735.2

PENG Dongge, WAN Ziye, LU Ning. Artificial intelligence in gastric cancer diagnosis, treatment and prognostic prediction: current application and future perspective[J]. China Oncology, 2025, 35(5): 496-504.

Figures/Tables 3

Fig.1

Fig. 2

Tab. 1

Research progress of AI combined with omics data in gastric cancer"

Application	Research content	AI method	Results
Endoscopic diagnosis	Validation of AI model consistency with pathological diagnosis of gastric lesions^[16]	U-Net deep learning model	The AI model exhibited superior consistency with pathological diagnoses during real-time endoscopic video monitoring, surpassing endoscopists in diagnostic accuracy
	Effectiveness of AI-assisted real-time monitoring and diagnosis of endoscopic lesions ^[19]	CNN-based deep learning model	The AI-assisted system achieved an accuracy of 84.7%, sensitivity of 100%, and specificity of 84.3% in gastric cancer detection, enhancing the quality of endoscopic examinations
Imaging diagnosis	Prediction of preoperative lymph node metastasis in gastric cancer^[30]	DLRN based on CNN	DLRN effectively distinguished lymph node metastasis stages in advanced gastric cancer, outperforming clinical N staging methods and showing significant correlation with OS
	Prediction of occult peritoneal metastasis in advanced gastric cancer^[35]	GAN-based deep learning model (PMetNet)	The nomogram model, incorporating Lauren classification and tumor differentiation, significantly improved the diagnostic accuracy of preoperative occult peritoneal metastasis, with AUC values of 0.950 and 0.953, respectively
Pathological diagnosis	Development of an AI-assisted rapid pathological diagnosis system^[40]	CNN-based deep learning pathology diagnostic system	The system rapidly identified suspicious tumor regions, demonstrating robust diagnostic performance (AUC: 0.986; accuracy: 0.873; sensitivity: 0.996; specificity: 0.806)
	Identification of lymph nodes and tumor regions to assist in gastric cancer lymph node metastasis diagnosis^[42]	CNN-based deep reinforcement learning	The model achieved a sensitivity of 98.5% and specificity of 96.1%, significantly reducing the time required for pathologists to diagnose lymph node metastasis
Therapeutic response prediction	Prediction of targeted therapy efficacy in HER2-positive gastric cancer patients^[46]	Hybrid predictive model (MuMo) based on CNN and Transformer architecture	MuMo exhibited AUC values of 0.821 and 0.914 in predicting responses to targeted therapy in HER2-positive gastric cancer patients, effectively stratifying them into high-risk and low-risk groups
Prognostic assessment	Development of a RFS prediction model for advanced gastric cancer patients based on radiomic features^[54]	CNN-based deep learning model	The model optimized RFS prediction and high-risk stratification in gastric cancer patients

Tab. 1

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