Evidence map›Paper›PMID 41987267›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026

Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study.

Jia He, Xianzheng Tan, Huashan Lin, Jing Fang, Diejuan Liu, Jiabei Liu, Xiang Feng, Xiaoping Yu, Peng Liu

Abstract readMulticenter Study
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. 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. Review
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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

9 authors.

Jia He *Department of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), No. 61 Jiefang West Road, Changsha, Hunan Province, 410005, China.
Xianzheng Tan *Department of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), No. 61 Jiefang West Road, Changsha, Hunan Province, 410005, China.
Huashan LinGE Healthcare, Hangzhou, Zhejiang Province, China.
Jing FangDepartment of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), No. 61 Jiefang West Road, Changsha, Hunan Province, 410005, China.
Diejuan LiuDepartment of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), No. 61 Jiefang West Road, Changsha, Hunan Province, 410005, China.
Jiabei LiuDepartment of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), No. 61 Jiefang West Road, Changsha, Hunan Province, 410005, China.
Xiang FengDepartment of Radiology, Hunan Women and Children's Hospital, Changsha, Hunan Province, China.
Xiaoping YuDepartment of Radiology, Hunan Cancer Hospital, Changsha, Hunan Province, 410013, China. yuxiaoping@hnca.org.cn.
Peng LiuDepartment of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), No. 61 Jiefang West Road, Changsha, Hunan Province, 410005, China. lpradiology@163.com.

Funding

Changsha Municipal Natural Science Fundation kq2014201
6 · The paper itself

Abstract

objectivesTo develop and validate a magnetic resonance imaging (MRI)-based radiomics model of the mesorectum for predicting extramural venous invasion (EMVI) in patients with rectal cancer (RC).

methodsA retrospective study included 238 patients with RC from two hospitals between May 2020 and March 2023. Patients were divided into a training set (n = 114, from institution 1), an internal validation set (n = 48, from institution 1), and an external validation set (n = 76, from institution 2). A total of 963 radiomics features were extracted from the mesorectum region using T2-weighted imaging (T2WI). The radiomics model was developed using the methods of the minimum redundancy of the maximum relevance (mRMR) and the least absolute shrinkage (LASSO) regression. After univariate and multivariate logistic analysis, a clinical model was constructed based on clinical characteristics. A combined model was built and demonstrated as a nomogram. These models were evaluated by discrimination, calibration, and clinical application.

resultsAmong 238 patients, 98 (41.1%) were EMVI-positive. The Area Under the Curve (AUC) values for the clinical, radiomics, and combined models, respectively, were 0.65, 0.85, and 0.88 for the training set (95% CI: 0.81-0.94); 0.60, 0.81, and 0.81 for the internal validation set (95% CI: 0.68-0.95); and 0.60, 0.78, and 0.82 for the external validation set (95% CI: 0.72-0.91).

conclusionThis study presents a model for predicting the EMVI status in patients with rectal cancer. The combined model, which incorporates both a mesorectal radiomics signature from T2WI and the clinical predictor of serum neutrophil count, demonstrated superior discrimination, calibration, and clinical utility compared to models based on either clinical or radiomics features alone. The non-invasive tool shows promise for aiding in preoperative risk stratification and guiding clinical decision-making.

Indexed as

Magnetic Resonance ImagingRectal NeoplasmsAgedFemaleHumansMaleMiddle AgedNeoplasm InvasivenessNomogramsRadiomicsRectumRetrospective StudiesArtificial intelligenceExtramural venous invasionMagnetic resonance imagingMesorectumRectal cancer

Identifiers

PMID41987267
PMCPMC13192049

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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.