ReviewInsights into imaging2025
The influence of AI on surgical and ablative treatments for colorectal liver metastases: a review of the current literature.
Review in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Development and validation of a multivariable CT-based Delta-radiomics model for predicting efficacy to bevacizumab therapy in patients with colorectal liver metastases.Translational cancer research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesArtificial 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. MATERIALS AND
methodsA 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.
resultsOut 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.
conclusionsAI 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. CRITICAL RELEVANCE STATEMENT: 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. KEY POINTS: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.