Contemporary Strategies in the AI era for the Management of Colorectal Liver Metastases: From Multidisciplinary Decision-Making to Minimally Invasive Surgery

Colorectal liver metastases (CRLM) represent the most frequent site of distant spread from colorectal cancer and remain a major determinant of patient prognosis. Over the past decade, major advances in systemic chemotherapy, targeted and immunotherapeutic agents, and surgical and ablative strategies have led to unprecedented improvements in survival. The management of CRLM has transitioned from a purely surgical discipline to a truly multidisciplinary process involving surgeons, medical oncologists, radiologists, hepatologists, and pathologists. Despite these advances, significant heterogeneity still exists in patient selection, treatment sequencing, and the adoption of minimally invasive surgery. The increasing availability of artificial intelligence (AI)–driven tools and data-rich clinical registries now offers new opportunities to refine perioperative risk prediction, optimize timing between surgery and systemic therapy, and standardize treatment algorithms across healthcare settings.



Recent studies have demonstrated the potential of laparoscopic and robotic liver resections to achieve oncologic results comparable to open surgery, with advantages in postoperative recovery, complications, and quality of life. When combined with advanced imaging, 3D planning, and augmented reality, minimally invasive approaches may offer superior precision and safety in complex and repeat hepatectomies. Parallel progress in perioperative optimization, such as Enhanced Recovery After Surgery (ERAS) pathways, has improved outcomes and reduced hospital stays. Moreover, artificial intelligence applications—including machine learning models for resectability prediction, radiomics for tumor characterization, and decision-support systems—are gradually entering the clinical workflow. Nonetheless, the translation of these innovations into daily practice remains inconsistent, highlighting the need for shared protocols, integrated decision-making, and high-quality evidence from real-world and multicenter experiences.



This Research Topic aims to gather and disseminate cutting-edge knowledge on the modern management of colorectal liver metastases in the era of AI and minimally invasive surgery. It seeks to define evidence-based pathways that combine technological innovation with multidisciplinary collaboration to enhance outcomes, safety, and value-based oncologic care. Key objectives are to highlight novel patient selection strategies, optimal sequencing with systemic therapies, and the role of local treatments in complex metastatic disease, while also exploring how data analytics and AI can refine decision-making across disciplines.



To gather further insights into how multimodal and data-driven management can transform CRLM care, we welcome Original Research, Review, Mini Review, Perspective, and high-quality Methodology articles addressing—but not limited to—the following themes:



Multidisciplinary decision-making and treatment algorithms integrating surgery, oncology, and radiology

Patient selection, resectability criteria, and timing in the era of modern systemic and targeted therapies

Role and outcomes of minimally invasive liver surgery (laparoscopic and robotic) compared to open approaches

Complex and extended resections, parenchymal-sparing strategies, and repeat hepatectomy

Role of local and hybrid treatments, such as ablation and staged or combined procedures

Use of artificial intelligence, predictive analytics, and radiomics for surgical planning and oncologic prognostication

Centralization of care, volume–outcome relationships, and international collaborative models

Perioperative optimization, Enhanced Recovery After Surgery (ERAS) protocols, and patient-centered outcomes

This Research Topic will provide a platform to harmonize innovations across disciplines and define the next frontier in precision and minimally invasive management of colorectal liver metastases.



Please note: manuscripts consisting solely of bioinformatics or computational analysis of sole public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.