Advances in Surgical Techniques and ML/DL-based Prognostic Biomarkers for Surgical and Adjuvant Therapies of Hepatobiliary and Pancreatic Cancers

Hepatobiliary and pancreatic cancers, including hepatocellular carcinoma (HCC), pancreatic ductal adenocarcinoma (PDAC), and cholangiocarcinoma (CCA), remain among the most aggressive and lethal malignancies globally. Despite advancements in oncological treatment strategies, surgical intervention remains the primary curative option for these cancers. Innovations in surgical techniques, such as minimally invasive liver resection, vascular replacement in advanced PDAC, and improved transplant eligibility criteria for HCC, have significantly influenced patient outcomes. However, challenges persist in surgical feasibility, perioperative management, and recurrence prevention.

In parallel, the emergence of artificial intelligence (AI)-driven approaches, particularly machine learning (ML) and deep learning (DL) models, has revolutionized prognostic biomarker discovery. These models can integrate multi-omics data, imaging features, and clinical parameters to enhance risk stratification, predict recurrence, and refine patient selection for surgical and adjuvant therapies. The integration of AI-driven prognostics with surgical advancements holds great promise in hepatobiliary and pancreatic cancer management.

This Research Topic aims to highlight recent breakthroughs in surgical innovations and AI-based prognostic biomarker research to improve clinical decision-making and patient survival. We invite original research, clinical studies, and comprehensive reviews on topics including, but not limited to:
1. ML/DL-based Prognostic Biomarkers: Development and validation of AI-driven biomarkers for predicting postoperative outcomes, recurrence risk, and therapy response.
2. Imaging-Based AI Models: Application of ML/DL algorithms in radiomics, pathology, and intraoperative imaging for tumor characterization and surgical planning.
3. Biomarker-Driven Surgical Decision Making: Integration of molecular and novel biomarkers to refine patient selection criteria for surgery.
4. Postoperative Surveillance and AI: AI-powered tools for monitoring recurrence and long-term prognosis following surgical resection or transplantation.

By bringing together insights from surgery, oncology, and computational medicine, this Research Topic seeks to foster interdisciplinary collaborations and accelerate the translation of innovative techniques and AI-based biomarkers into clinical practice.

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.

Keywords: Hepatobiliary Cancer, Pancreatic Cancer, Biomarker, Surgical Techniques, TME Metabolism

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.