# The influence of AI on surgical and ablative treatments for colorectal liver metastases: a review of the current literature

## TL;DR

This review explores how AI is currently being used to support surgical and ablation treatments for colorectal liver metastases, highlighting its potential and limitations.

## Contribution

The paper provides a scoping review of AI applications in CRLM treatment strategies, identifying gaps and future research directions.

## Key findings

- Most AI studies in CRLM use traditional machine learning with radiomics for predictive modeling.
- Current AI applications are limited by small sample sizes and lack robustness for generalization.
- Future research should focus on large multicenter studies to validate AI tools for CRLM treatment.

## Abstract

Artificial intelligence (AI) has gained increasing interest in supporting clinicians in patient selection, treatment planning, and prognostics. However, the current application of AI in the treatment of colorectal liver metastases (CRLM) is not clearly established and validated. This scoping review assesses the current impact of AI techniques on local therapeutic strategies for CRLM, exploring its benefits, challenges, and future directions.

A comprehensive literature search in PubMed, EMBASE, Web of Science, and SCOPUS was conducted for patients with CRLM undergoing surgery or thermal ablation, along with descriptions of AI tools. Eligible studies were cohort studies or clinical trials. Data extraction focused on treatment strategies, AI techniques, clinical and radiomics parameters, and outcomes.

Out of 1464 articles, thirteen met the inclusion criteria. Eight articles regarded thermal liver ablation, and five surgical resection for CRLM. Most studies used traditional machine learning methods, such as support vector machines and random forests, combined with radiomics for predictive model building. While most studies demonstrated high performance, they frequently involved small sample sizes, and machine learning techniques often lacked robustness.

AI shows promising results in improving local treatment strategies for CRLM, but further advancements are required for AI decision support tools. Future research should focus on large multicentre studies to validate AI-driven personalised colorectal liver metastases treatment strategies.

This review evaluates the use of AI in colorectal liver metastases treatment for outcome prediction and treatment evaluation. Included studies used AI for segmentation and predictive modelling and were often limited by small sample-sized single-centre studies, necessitating multicentre studies.

The position of AI in local colorectal liver metastases treatments is not yet established.AI algorithms are mostly used for predictive model building and image registration.Studies often lack validity, limiting generalisability and implementation of AI support tools.

The position of AI in local colorectal liver metastases treatments is not yet established.

AI algorithms are mostly used for predictive model building and image registration.

Studies often lack validity, limiting generalisability and implementation of AI support tools.