Evidence map›Paper›PMID 41114093›Full record

ArticleWorld journal of gastrointestinal oncology2025

Multiparametric magnetic resonance imaging-based predictive model for chemotherapy response in colorectal cancer patients with gene mutations.

Wen-Yan Kang, Wen-Ming Deng, Xiao-Qin Ye, Yi-Hong Zhong, Xiao-Jun Li, Ling-Ling Feng, De-Hong Luo

Abstract read
In one paragraph

Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

7 authors.

Wen-Yan KangDepartment of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China.
Wen-Ming DengDepartment of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China.
Xiao-Qin YeDepartment of Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China.
Yi-Hong ZhongDepartment of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China.
Xiao-Jun LiDepartment of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China.
Ling-Ling FengDepartment of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China.
De-Hong LuoDepartment of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China. luodehong2024@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients harboring gene mutations like

aimTo create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.

methodsThis retrospective study was conducted in a tertiary hospital, analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023. Based on chemotherapy outcomes, the patients were categorized into favorable (

resultsUnivariate analysis identified that tumor differentiation, T2 signal intensity ratio, tumor-to-anal margin distance, and MRI-detected lymph node metastasis as significantly associated with chemotherapy response (

conclusionWe established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations. This model holds promise for guiding individualized treatment strategies.

Indexed as

ChemotherapyColorectal cancerMultiparametric magnetic resonance imagingPredictive modelRAS gene mutation

Identifiers

PMID41114093
PMCPMC12531773

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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.