Evidence map›Paper›PMID 40167935›Full record

ArticleLa Radiologia medica2025

A comprehensive study on the radiomic score derived from perineural invasion in gastric cancer and its correlation with the overall survival of patients.

Yueling Wang, Xuhui Fan, Zai Luo, Qingguo Wang, Yuan Fang, Chao Han, Zhengjun Qiu, Han Wang, Chen Huang

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Article in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Machine Learning-Based Pathomics Signature for Perineural Invasion in Colorectal Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2025
    Article
4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yueling WangDepartment of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Xuhui FanDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Zai LuoDepartment of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Qingguo WangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Yuan FangDepartment of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Chao HanDepartment of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Zhengjun QiuDepartment of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Han WangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China. han.wang@shsmu.edu.cn.
Chen HuangDepartment of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China. huangchen0204@sjtu.edu.cn.

Funding

Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support 2019142the National Natural Science Foundation of China No.82072662
6 · The paper itself

Abstract

backgroundPerineural invasion (PNI) is closely related to the prognosis of gastric cancer (GC) patients. However, a noninvasive tool for accurately and reliably predicting the PNI is lacking.

methodsThe clinical and imaging data of 278 patients from institution I and 39 patients from institution II were retrospectively analyzed. Radiomic features were extracted from the intratumoral and peritumoral regions. Seven independent machine learning (ML) algorithms are used to develop the models. Kaplan-Meier survival analysis and Cox proportional hazards analysis were carried out to compare 3-year and 5-year overall survival (OS) differences among various subgroups based on PNI and radiomic scores.

resultsT stage and lymphovascular invasion (LVI) were significantly correlated with the PNI (P < 0.01). The OS of patients with different PNI status was significantly different (P < 0.05). Gradient boosting tree is the best ML algorithm. The area-under-the-curve (AUC) values of the optimal radiomics model in the internal test set and external test set were 0.901 and 0.886, respectively. After the introduction of clinical variables T stage and LVI, the performance of the model further improved in predicting the PNI of GC patients, with the AUC of 0.904 in the internal test set and 0.886 in the external test set. The difference in 3-year OS (P = 0.005) and 5-year OS (P = 0.015) among patients with varying radiomic scores was statistically significant.

conclusionRadiomics combined with intratumoral and peritumoral features is feasible for evaluating the PNI of GC patients. The prognosis of patients with different radiomic scores was statistically significant.

Indexed as

Peripheral NervesStomach NeoplasmsTomography, X-Ray ComputedAdultAgedAged, 80 and overAlgorithmsFemaleHumansKaplan-Meier EstimateMachine LearningMaleMiddle AgedNeoplasm InvasivenessNeoplasm StagingPrognosisComputed tomographyGastric cancerPerineural invasionRadiomics

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PMID40167935

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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.