Evidence map›Paper›PMID 41741564›Full record

ArticleScientific reports2026

Prediction of colorectal cancer liver metastasis through an MRI radiomic model.

Yao-Kun Wu, Xue Wang, Pei-Zhuo Du, Peng Zhang, Ning Liu, Yun-Yun Tao, Jing Zheng, Xiao-Hua Huang, Lin Yang

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yao-Kun Wu *Interventional Medical Center, Department of Radiology, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, The People's Republic of China.
Xue Wang *Department of Oncology, Mianyang Fulin Hospital, Mianyang Fulin Hospital Co., Ltd., Mianyang, 621000, The People's Republic of China.
Pei-Zhuo DuDepartment of Radiology, Nanchong Central Hospital, Nanchong, 637000, The People's Republic of China.
Peng ZhangInterventional Medical Center, Department of Radiology, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, The People's Republic of China.
Ning LiuInterventional Medical Center, Department of Radiology, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, The People's Republic of China.
Yun-Yun TaoInterventional Medical Center, Department of Radiology, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, The People's Republic of China.
Jing ZhengInterventional Medical Center, Department of Radiology, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, The People's Republic of China.
Xiao-Hua HuangInterventional Medical Center, Department of Radiology, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, The People's Republic of China.
Lin YangInterventional Medical Center, Department of Radiology, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, The People's Republic of China. linyangmd@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to investigate the efficacy of a magnetic resonance imaging (MRI) radiomic model in predicting colorectal cancer liver metastasis (CRLM). Two independent cohorts consisting of 194 patients with pathologically confirmed colorectal cancer (CRC) who underwent baseline MRI examinations were recruited from the Affiliated Hospital of North Sichuan Medical College (Unit 1, n = 159) and Nanchong Central Hospital (Unit 2, n = 35) and divided into a training cohort (Unit 1) and an independent external validation cohort (Unit 2). The clinical risk factors for all patients were examined via univariate and multivariate analyses to identify independent clinical risk factors for CRLM. Radiomic features from oblique axial or axial fat-free T

Indexed as

Colorectal NeoplasmsLiver NeoplasmsMagnetic Resonance ImagingAgedDiffusion Magnetic Resonance ImagingFemaleHumansMaleMiddle AgedRadiomicsRisk FactorsROC CurveColorectal cancerLiver metastasisMRIRadiomics

Identifiers

PMID41741564
PMCPMC13046780

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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.