Magnetic resonance imaging radiomics-based machine learning model for preoperative prediction of pathological grade in pancreatic cancer: A preliminary study.

Jiayi Bao, Yuanqing Liu, Yang Yu, Ximing Wang, Chunhong Hu, Su Hu

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Article in European journal of radiology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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1 citing paper in PubMed.

Beyond conventional imaging: curvelet-AI convergence for non-invasive microalgal lipid quantification and biofuel optimization.Bioprocess and biosystems engineering · 2026
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5 · Who and what money

Authors and funding

6 authors.

Jiayi BaoDepartment of Radiology, the First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, Jiangsu Province 215006, China.

Yuanqing LiuDepartment of Radiology, the First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, Jiangsu Province 215006, China.

Yang YuDepartment of Radiology, the First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, Jiangsu Province 215006, China.

Ximing WangDepartment of Radiology, the First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, Jiangsu Province 215006, China.

Chunhong HuDepartment of Radiology, the First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, Jiangsu Province 215006, China.

Su HuDepartment of Radiology, the First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, Jiangsu Province 215006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To explore the value of MRI radiomics-based machine learning models for predicting the pathological grade of pancreatic cancer preoperatively. Methods: 125 patients with pathologically confirmed pancreatic cancer who underwent preoperative MRI were retrospectively enrolled. The primary cohort was randomized in an 8:2 ratio into a training cohort (n = 100) and a validation cohort (n = 25). 1316 radiomics features were extracted from contrast-enhanced T Results: There were no statistical differences in clinical characteristics between the low-grade and high-grade cohorts ( Conclusions: MRI radiomics-based machine learning model is a potential non-invasive method to predict the pathological grade of pancreatic cancer.

Indexed as

Machine learningMagnetic resonance imagingPancreatic cancerRadiomics

Identifiers

PMID41278430

DOI10.1016/j.ejro.2025.100703

PMCPMC12639551

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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.