Artificial Intelligence in Immunotherapy for Gastrointestinal Cancers: From Prediction to Precision Medicine

About this Research Topic

Background

Gastrointestinal cancers continue to pose a significant global health challenge, with limited effective treatment options and poor prognoses for many patients. Immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic approach; however, their efficacy remains variable, with only a subset of patients achieving durable responses. Traditional biomarkers, such as PD-L1 expression and tumor mutational burden (TMB) have provided some predictive value but suffer from limitations in accuracy, reproducibility, and clinical accessibility.

Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning, are revolutionizing precision oncology by enabling the integration of multi-dimensional data, including routine clinical parameters, multi-omics profiles (genomic, transcriptomic, proteomic, radiomic, and immunogenetic data), and digital pathology features. These AI-driven models can uncover complex patterns and interactions that elude conventional statistical methods, allowing for more accurate patient stratification, prediction of immunotherapy response, prognosis assessment, and even early detection of adverse events. For instance, recent studies have demonstrated that multi-dimensional predictive models integrating clinical, molecular, and imaging data significantly outperform single-biomarker approaches in predicting ICI efficacy, treatment resistance, and survival outcomes. Furthermore, AI-powered decision-support systems could optimize therapeutic strategies by dynamically incorporating real-world evidence and emerging biomarkers, ultimately improving clinical outcomes for gastrointestinal cancer patients.

This Research Topic aims to showcase cutting-edge research on AI and ML applications in GI cancer immunotherapy. We seek studies that leverage computational approaches to improve patient stratification, predict treatment response, uncover resistance mechanisms, and mitigate adverse events. By bridging the gap between computational innovation and clinical practice, this collection will foster the development of robust, interpretable AI tools that can be seamlessly integrated into real-world oncology workflows.

We welcome original research and methodological advances focusing on:
• Predictive modeling using clinical, imaging, or omics data to forecast ICI response in GI cancers.
• Biomarker discovery through AI-driven analysis of genomic, transcriptomic, or proteomic datasets.
• Toxicity prediction to identify patients at risk for severe irAEs.
• Multi-modal data fusion to enhance the accuracy of AI models.
• Clinical translation of AI tools, including validation in prospective trials and real-world cohorts.

Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Immunotherapy, Gastrointestinal Cancer, Predictive Biomarkers, Machine Learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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