[Artificial intelligence for lymph node metastasis prediction in gastric cancer: research progress]

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Gastric cancer is a prevalent malignancy in China, with lymph node metastasis (LNM) significantly impacting patient prognosis.
• Accurate preoperative prediction of LNM is crucial for treatment planning, staging, and prognosis estimation in gastric cancer.
• Current methods for LNM evaluation in gastric cancer suffer from low accuracy and high subjectivity.

Purpose of the Study:

• To provide a comprehensive overview of the application of artificial intelligence (AI) in predicting lymph node metastasis (LNM) in gastric cancer.
• To review the current status and recent advancements of AI-driven LNM prediction models.
• To discuss the challenges and future directions for AI in gastric cancer LNM assessment.

Main Methods:

• Review of existing literature on AI applications for gastric cancer LNM prediction.
• Analysis of studies utilizing multimodal data (clinical, imaging, pathomics, molecular) with AI algorithms.
• Synthesis of findings regarding AI model performance and clinical utility.

Main Results:

• AI, particularly when integrating multimodal data, demonstrates potential to enhance the accuracy of LNM diagnosis in gastric cancer.
• AI-assisted decision-making shows significant recent progress in improving preoperative LNM risk assessment.
• AI offers a promising avenue for more precise and individualized medicine in gastric cancer management.

Conclusions:

• AI holds significant potential to improve the accuracy and objectivity of lymph node metastasis prediction in gastric cancer.
• The integration of multimodal data with AI is key to advancing LNM diagnosis and clinical decision-making.
• Further research and development are needed to overcome challenges and fully realize the benefits of AI in gastric cancer care.