ReviewLife (Basel, Switzerland)2023
Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives.
Review in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Imaging-based 3D-printed anatomical models for preoperative planning in hepatopancreatobiliary surgery: a single-center pilot study, cost analysis, and systematic review.La Radiologia medica · 2026Pooled it
- Improving cost-efficiency in port-site fascial closure: a novel Veress-needle technique and a comprehensive literature review.Updates in surgery · 2026Article
- Radiomics of portal-phase ring enhancement: a novel imaging biomarker for bevacizumab response associated with overall survival rates. It might help with surgical decision-making in colorectal liver metastases?Updates in surgery · 2026Article
- How to Integrate Surgery into the Multidisciplinary Treatment of Liver-Only Metastatic Colorectal Cancer.Cancers · 2026Review
- Contrast-enhanced ultrasound perfusion quantification of solid liver lesions: First intraoperative characterization of tumor microvascularization.Clinical hemorheology and microcirculation · 2025Article
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- Review
- Diagnostic accuracy of artificial intelligence based on imaging data for predicting distant metastasis of colorectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Article
- Contrast-Enhanced Intraoperative Ultrasound Shows Excellent Performance in Improving Intraoperative Decision-Making.Life (Basel, Switzerland) · 2024Article
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- The role of superior hemorrhoidal vein ectasia in the preoperative staging of rectal cancer.Frontiers in oncology · 2024Article
- Colorectal Cancer: Current Updates and Future Perspectives.Journal of clinical medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM.
methodsA narrative review of the literature was conducted on PubMed. A total of 336 studies were screened.
resultsWe selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT).
conclusionsRoutinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.
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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 OpenQuestion graph.