Integrating tumor location into artificial intelligence-based prognostic models in cancer

Area of Science:

• Oncology
• Medical Informatics
• Biostatistics

Background:

• Tumor location is increasingly recognized as a prognostic factor in gastric cancer.
• Huang et al. demonstrated the correlation between tumor location and patient prognosis post-surgery.
• Proximal gastric tumors are associated with shorter survival periods and inferior outcomes.

Discussion:

• Gender-based differences in tumor markers, such as carbohydrate antigen 72-4, suggest a sex-specific influence on tumor location's prognostic value.
• The precise role of tumor location in clinical prediction models for various cancers requires further elucidation.
• Integrating tumor location into artificial intelligence (AI)-based prognostic tools offers a promising avenue for enhancing predictive accuracy.

Key Insights:

• Tumor location, particularly in the proximal stomach, significantly impacts gastric cancer patient prognosis.
• Sex-specific differences in tumor markers underscore the complexity of tumor location's influence.
• AI-driven prognostic models incorporating tumor location have the potential to refine clinical decision-making.

Outlook:

• A stepwise framework is proposed for developing AI-based prognostic models, encompassing retrospective training, prospective validation, and clinical implementation.
• Addressing technical, ethical, and interoperability challenges is crucial for the successful real-world application of these advanced prognostic tools.
• Further research is warranted to fully integrate tumor location into comprehensive cancer prognosis strategies.