ReviewiLIVER2025
Advances in magnetic resonance imaging for the evaluation of colorectal liver metastases in context of individualized precision medicine.
Review in iLIVER, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal liver metastases (CRLM) represent a significant clinical challenge, as they are a leading cause of morbidity and mortality in patients with colorectal cancer (CRC). Early detection, accurate diagnosis, and precise treatment planning are crucial for improving patient outcomes. Magnetic resonance imaging (MRI) has emerged as a cornerstone in evaluating CRLM. This article provides a comprehensive review of recent innovations in MRI for CRLM diagnosis and treatment, with a particular focus on precision surgical models. Additionally, the application of artificial intelligence (AI) and radiomics is explored, highlighting their potential in automating lesion detection, evaluating treatment response, and predicting patient survival. The integration of these advanced imaging techniques and AI-based models holds promise for enhancing clinical decision-making, enabling personalized treatment strategies, and improving patient outcomes in CRLM. As these technologies continue to evolve, they could revolutionize the management of CRLM, offering non-invasive, accurate, and cost-effective solutions for early detection, monitoring, and prognosis prediction in CRC patients.
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.