Evidence map›Paper›PMID 40410525›Full record

ArticleDiscover oncology2025

Predictive value of combined DCE-MRI perfusion parameters and clinical features nomogram for microsatellite instability in colorectal cancer.

Leping Peng, Wenting Ma, Xiuling Zhang, Fan Zhang, Fang Ma, Kai Ai, Xiaomei Ma, Yingmei Jia, Hong Ou-Yang, Shengting Pei and 3 more

Abstract read
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

13 authors.

Leping Peng *Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
Wenting Ma *Department of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
Xiuling ZhangGansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
Fan ZhangGansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
Fang MaGansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
Kai AiDepartment of Clinical and Technical Support, Philips Healthcare, Xi'an, 710065, Shanxi, China.
Xiaomei MaDepartment of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
Yingmei JiaDepartment of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
Hong Ou-YangDepartment of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
Shengting PeiDepartment of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
Tao WangDepartment of Colorectal Surgery, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China.
Yuanhui ZhuDepartment of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China. zyh960419@163.com.
Lili WangDepartment of Radiology, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China. wanglilihq@163.com.

Funding

Gansu Provincial Department of Education: Graduate Student "Innovation and Entrepreneurship" Project of Gansu University of Chinese Medicine No. 2025CXCY-071Gansu Provincial Hospital Research Fund Project No.23GSSYA-2Gansu Provincial Hospital Research Fund Project No.23GSSYF-4Gansu Provincial Youth Science and Technology Fund Project No.20JR5RA143
6 · The paper itself

Abstract

objectivesTo develop a nomogram that combines dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters, ADC values and clinical features to preoperatively identify microsatellite instability (MSI) in patients with colorectal cancer (CRC).

methodsThis retrospective study included 63 CRC patients who underwent preoperative DCE-MRI and had immunohistochemistry results available. Two radiologists, in a double-blind manner, placed two circular regions of interests in the area with the highest perfusion intensity on the DCE-MRI perfusion map and the corresponding area on the ADC map. Perfusion parameters and ADC values were measured, and the average values from both radiologists were used for subsequent analysis. Univariate analysis was performed to identify independent risk factors for MSI. A nomogram was then constructed by combining the most significant clinical risk factors with DCE-MRI perfusion parameters. The model's performance was evaluated using receiver operating characteristic (ROC) curves. Calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to assess the nomogram's clinical utility and net benefit.

resultsThe nomogram prediction model, which combined PLT, LNM, K

conclusionThe nomogram method demonstrated good potential in the preoperative individualized identification of MSI status in CRC patients. This tool can assist clinicians in adopting appropriate treatment strategies and optimizing personalized stratification for CRC patients.

Indexed as

Colorectal cancerDCEMagnetic resonance imagingMicrosatellite instabilityNomogram

Identifiers

PMID40410525
PMCPMC12102045

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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.