Topic: AI-Driven Decision-Making in Minimally Invasive Cancer Surgery: Integrating Tumor Biology into Surgical Planning and Outcomes

Minimally invasive cancer surgery has undergone remarkable evolution over the past decades, driven by advances in surgical techniques, instrumentation, and perioperative management. While reduced surgical trauma, faster recovery, and improved short-term outcomes are now well established, contemporary challenges increasingly lie not in how to operate, but in determining when, where, and to what extent surgical intervention should be performed. These questions underscore the growing need for data-informed and biology-aware surgical decision-making. 

Recent progress in artificial intelligence (AI), multi-omics profiling, and computational modeling has enabled unprecedented insights into tumor biology, heterogeneity, and the tumor microenvironment. These advances have reshaped modern oncology by supporting precision diagnosis, risk stratification, and treatment selection. However, the translation of tumor biological information into actionable guidance for minimally invasive surgical planning remains limited and fragmented. Bridging this gap represents a critical frontier for the next generation of minimally invasive surgery.

AI-driven analytical frameworks now offer powerful tools for integrating complex biological data—including genomic, transcriptomic, immunological, and metabolic features—with clinical and imaging information. When aligned with minimally invasive surgical workflows, these approaches have the potential to inform key decisions such as patient selection, optimal surgical timing, extent of resection, lymphadenectomy strategies, and the prediction of postoperative outcomes. Rather than replacing surgical expertise, AI-supported decision-making can enhance surgical precision by providing objective, individualized risk assessments and outcome projections. 

This Special Issue focuses on AI-enabled decision-making in minimally invasive cancer surgery, with an emphasis on translating tumor biology into practical surgical strategies and optimizing outcomes. We particularly welcome studies that demonstrate clear relevance to surgical planning, intraoperative decision support, and postoperative outcome assessment in minimally invasive procedures. Contributions may include original research articles, systematic reviews, and translational studies that connect biological insights with real-world surgical applications. 

Topics of interest include, but are not limited to:

● AI-assisted risk stratification and patient selection for minimally invasive cancer surgery;

● Integration of tumor biological features into surgical planning and extent-of-resection decision-making;

● Predictive models combining imaging, pathology, and molecular data to guide minimally invasive approaches;

● AI-supported assessment of surgical margins, lymph node management, and functional preservation;

● Postoperative outcome prediction, recurrence risk assessment, and long-term oncologic outcomes;

● Translational frameworks linkingtumor biology, computational analysis, and minimally invasive surgical practice.

By highlighting research that directly informs surgical decision-making rather than focusing solely on technical execution, this Special Issue aims to advance a more intelligent, personalized, and outcome-oriented paradigm for minimally invasive cancer surgery. We hope this collection will stimulate interdisciplinary collaboration and provide practical insights to support surgeons in delivering safer, more precise, and biologically informed care.