Evidence map›Paper›PMID 40991117›Full record

ArticleInsights into imaging2025

MRI-based risk stratification for predicting overall survival in pancreatic ductal adenocarcinoma.

Haitao Sun, Jianbo Li, Lei Li, Peng Wang, Qiying Tang, Tianyu Lu, Jun Han, Zongyu Xie, Yiheng Zhou, Kai Liu and 4 more

Abstract read
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Haitao Sun *Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Jianbo Li *Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Lei Li *Department of Radiology, Fengyang County People's Hospital, Chuzhou, China.
Peng WangDepartment of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, 214122, China.
Qiying TangDepartment of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen Municipal Clinical Research Center for Medical Imaging, Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen, 361015, China.
Tianyu LuDepartment of Radiology, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China.
Jun HanDepartment of Radiology, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China.
Zongyu XieDepartment of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Yiheng ZhouWuxi School of Medicine, Jiangnan University, Wuxi, 214122, China.
Kai LiuDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Mengsu ZengDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Minping HongDepartment of Radiology, Jiaxing Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.
Yaolin XuDepartment of pancreatic surgery, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Jianjun ZhouDepartment of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen Municipal Clinical Research Center for Medical Imaging, Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen, 361015, China. jianjunzhou2024@163.com.ORCID http://orcid.org/0009-0002-2894-1243

Funding

the Natural Science Foundation of Zhejiang Province LQN25H180008the Shanghai Municipal Health Commission Health Industry Clinical Research Special Program (Youth Project) 20244Y0003
6 · The paper itself

Abstract

purposeTo develop and validate a magnetic resonance imaging (MRI)-based pancreatic risk stratification model (M-PRiSM) for prognosis prediction and therapy guidance in pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND

methodsIn this retrospective multicenter study, a total of 540 patients with PDAC were enrolled. A Cox proportional hazards model was used to identify significant clinical-radiological predictors and develop an M-PRiSM risk score system. The performance of the model was assessed using Harrell's C-index, time-dependent area under the curve (AUC), and calibration curves, and was compared with existing clinical prediction metrics, including the 8

results540 PDAC patients were split into a development cohort (n = 282) and an internal validation cohort (n = 122), with 136 patients forming external cohort A (n = 80) and B (n = 56). The model, incorporating CA19-9 (hazard ratio (HR), 1.604, p = 0.047), margin (HR, 1.918, p = 0.001), tumor size (HR, 1.308, p = 0.003), and venous enhancement ratio (VER) (HR, 0.605, p = 0.047), was developed, showing superior predictive accuracy for OS with C-indexes of 0.73, 0.70, and 0.70, and 0.68 across four cohorts. The model outperformed the 8th AJCC staging and CA19-9 alone across validation cohorts (p < 0.001). High-risk patients exhibit the worst overall survival (OS) and a higher frequency of adverse pathological features, but benefited significantly from adjuvant therapy, with improved survival outcomes.

conclusionThe M-PRiSM model effectively predicts postoperative OS and identifies PDAC patients likely to benefit from adjuvant treatment. CRITICAL RELEVANCE STATEMENT: The M-PRiSM model, utilizing MRI and clinical features, enables preoperative risk stratification for overall survival in pancreatic ductal adenocarcinoma, offering a generalizable tool to guide personalized adjuvant therapy and enhance prognostic assessment. KEY POINTS: The M-PRiSM model can effectively predict pancreatic ductal adenocarcinoma survival. The M-PRiSM model showed strong generalizability in external validations. The M-PRiSM model stratifies patients for guiding adjuvant therapy for improved outcomes.

Indexed as

Magnetic resonance imagingModelPancreatic ductal adenocarcinomaPrognosisResectable

Identifiers

PMID40991117
PMCPMC12460861

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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.