AI-Driven Innovation in Surgical Oncology: Diagnosis, Decision-Making, and Outcomes

Dear Colleagues,

Cancer surgery remains one of the most technically demanding and consequential disciplines in modern medicine. Despite significant advances in operative technique, anesthesia, and perioperative care, surgical outcomes in oncology continue to vary substantially across institutions, patient populations, and tumor types. Achieving clear margins, accurately staging disease, anticipating complications, and personalizing treatment pathways are persistent challenges that demand more than incremental improvement, and they demand transformative tools. Artificial intelligence (AI), encompassing machine learning, deep learning, computer vision, and natural language processing, is rapidly emerging as one of the most promising such tools available to the surgical oncologist.

Globally, the integration of AI into cancer care is accelerating. From preoperative imaging analysis and risk stratification to intraoperative guidance and robotic assistance, AI-powered systems are beginning to augment — and in some contexts surpass traditional clinical decision-making. Large-scale datasets derived from electronic health records, pathology archives, genomic profiling, and operative video are now enabling models that can predict recurrence, optimize resection strategies, and flag intraoperative anomalies in real time. At the same time, critical questions remain surrounding validation, equity, interpretability, and clinical integration, underscoring the urgent need for rigorous, multidisciplinary research.

This Special Issue aims to present and disseminate the most recent advances at the intersection of artificial intelligence and cancer surgery. We consider contributions addressing the translational spectrum, from algorithm development and computational modeling to clinical validation, implementation science, and ethical considerations in surgical AI.

Topics of interest for publication include, but are not limited to, the following:

• AI-assisted preoperative planning and surgical simulation in oncologic procedures
• Machine learning models for surgical risk stratification and outcome prediction
• Deep learning applications in intraoperative imaging, margin assessment, and tissue classification
• Computer vision and AI in robotic and laparoscopic cancer surgery
• Natural language processing for mining surgical and pathology reports in oncology
• AI-driven radiomics and pathomics for cancer staging and resectability assessment
• Predictive modeling for postoperative complications and recurrence after cancer surgery
• Ethical, regulatory, and equity considerations in AI-based surgical decision-making

We warmly invite original research articles and meta-analysis papers that advance our collective understanding of how AI can improve the safety, precision, and outcomes of cancer surgery. We look forward to receiving your contributions.

Dr. Chengli Shen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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