Evidence map›Paper›PMID 42256486›Full record

ArticleThe Lancet regional health. Western Pacific2026

A clinician-centric intelligent method towards reliable pancreatic cancer vascular invasion assessment: a retrospective, multi-centre study.

Rui Guo, Mengyao Zhang, Hongzhang Zhu, Xinlu Tang, Wenli Fu, Minhong Li, Huijun Hu, Xiang Xiao, Weishen Wang, Ying-Bin Liu and 3 more

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In one paragraph

Article in The Lancet regional health. Western Pacific, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Rui GuoSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Mengyao ZhangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Hongzhang ZhuDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xinlu TangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Wenli FuSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Minhong LiDepartment of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Huijun HuDepartment of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Xiang XiaoDepartment of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Weishen WangDepartment of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ying-Bin LiuDepartment of Biliary-Pancreatic Surgery, Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shi-Ting FengDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Tao ChenDepartment of Biliary-Pancreatic Surgery, Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaohua QianSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pancreatic cancer is highly aggressive, with high post-resection recurrence and poor survival. Accurate preoperative assessment of vascular invasion is essential but remains clinically unmet. This study aimed to enable reliable automated assessment of vascular invasion using routine computed tomography (CT). Methods: We developed CRVIA, a clinician-centric artificial intelligence (AI) method that encodes clinical expertise into invasion-omics and emulates clinician's decision-making through causality-enhanced modelling. The model was trained on 1251 cases and validated on internal (1104 cases) and external (3575 cases from five centres) cohorts. Reader studies involving eight radiologists were conducted to evaluate the efficacy of CRVIA's assistance. Primary outcome measures included classification performance, feature distance, and inter-reader agreement. Statistical analyses were conducted using paired Findings: The study cohort comprised 5930 cases from 2062 patients (median age 63.0 years [IQR 56.0-70.0]; 41.1% female). CRVIA achieved AUCs of 0.946 (95% CI 0.929-0.962) internally and 0.943 (95% CI 0.932-0.952) externally, outperforming nine comparison approaches spanning radiomics to foundation models. Performance remained stable across stages, vessel types, and segmentation variations. In reader studies, CRVIA exceeded senior radiologists' accuracy, elevated junior radiologists to expert-level performance, and substantially improved inter-reader agreement. Interpretation: This study presents a reliable, open-source AI tool (code: https://github.com/SJTUBME-QianLab/CRVIA) for accurate, stable, and interpretable vascular invasion assessment in pancreatic cancer, which potentially supports precise treatment, encourages collaborative research, and ultimately benefits patients, especially for resource-limited regions. Funding: National Natural Science Foundation of China and Shanghai Natural Science Foundation.

Indexed as

Artificial intelligenceComputed tomographyInvasion-omicsPancreatic cancerVascular invasion assessment

Identifiers

PMID42256486
PMCPMC13241961

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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.