Evidence map›Paper›PMID 40901348›Full record

ArticleWorld journal of radiology2025

Magnetic resonance imaging-based radiomics signature for predicting preoperative staging of esophageal cancer.

Ri-Hui Yang, Zhi-Ping Lin, Ting Dong, Wei-Xiong Fan, Hao-Dong Qin, Gui-Hua Jiang, Hai-Yang Dai

Abstract read
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Article in World journal of radiology, 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
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Ri-Hui YangDepartment of Magnetic Resonance, Meizhou People's Hospital, Meizhou 514031, Guangdong Province, China.
Zhi-Ping LinGE Healthcare, Guangzhou 510623, Guangdong Province, China.
Ting DongDepartment of Medical Imaging, Guangdong Second Province General Hospital, Guangzhou 510317, Guangdong Province, China.
Wei-Xiong FanDepartment of Magnetic Resonance, Meizhou People's Hospital, Meizhou 514031, Guangdong Province, China.
Hao-Dong QinSiemens Healthineers, Guangzhou 510317, Guangdong Province, China.
Gui-Hua JiangDepartment of Medical Imaging, Guangdong Second Province General Hospital, Guangzhou 510317, Guangdong Province, China.
Hai-Yang DaiDepartment of Radiology, Huizhou Central People's Hospital, Huizhou 516001, Guangdong Province, China. d.ocean@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEsophageal cancer (EC) is one of the most prevalent malignant gastrointestinal tumors; accurate prediction of EC staging has high significance before treatment.

aimTo explore a rational radiomic approach for predicting preoperative staging of EC based on magnetic resonance imaging (MRI).

methodsThis retrospective study included 210 patients with pathologically confirmed EC, randomly divided into a primary cohort (

resultsA total of 214 radiomics features were extracted. Following feature dimension reduction, the T1WI and T2WI sequences were retained, and 14 features from the T1WI sequence and 3 features from the T2WI sequence were selected to construct radiomics signatures. The radiomics signature combining T2WI with T1WI-Gd demonstrated superior discrimination of stages in the validation cohort (AUC: 0.851; SEN: 0.697; SPE: 0.793), which outperformed single-sequence models (AUC: 0.779, 0.844; SEN: 0.667, 0.636; SPE: 0.8, 0.8).

conclusionMRI-based radiomics signatures could identify EC stages before treatment, which could serve as a noninvasive and quantitative approach aiding personalized treatment planning.

Indexed as

Esophageal cancerLogistic regressionMagnetic resonance imagingRadiomicsTumor staging

Identifiers

PMID40901348
PMCPMC12400284

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