Evidence map›Paper›PMID 39488674›Full record

ArticleAbdominal radiology (New York)2026

Enhancing preoperative diagnosis of pancreatic ductal adenocarcinoma and mass-forming chronic pancreatitis: a study on normalized conventional MR imaging parameters.

Yuxiao Li, Chenxi Zheng, Yang Zhang, Tianlin He, Wei Chen, Kailian Zheng

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Article in Abdominal radiology (New York), 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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1 · What the graph read from it

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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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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuxiao Li *Department of Radiology, Changhai Hospital Affiliated to Navy Medical University, 168 Changhai Road, Shanghai, People's Republic of China.
Chenxi Zheng *Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital Affiliated to Navy Medical University, 168 Changhai Road, Shanghai, People's Republic of China.
Yang Zhang *Department of Oncology Radiation, Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, 528 Zhangheng Road, Shanghai, People's Republic of China.
Tianlin HeDepartment of Hepatobiliary Pancreatic Surgery, Changhai Hospital Affiliated to Navy Medical University, 168 Changhai Road, Shanghai, People's Republic of China.
Wei ChenDepartment of Radiology, Changhai Hospital Affiliated to Navy Medical University, 168 Changhai Road, Shanghai, People's Republic of China.
Kailian ZhengDepartment of Hepatobiliary Pancreatic Surgery, Changhai Hospital Affiliated to Navy Medical University, 168 Changhai Road, Shanghai, People's Republic of China. zhengkl828@yeah.net.ORCID 0009-0004-5457-6037

Funding

General Incubation Program of Basic Medical Research of Naval Medical University 2022MS013
6 · The paper itself

Abstract

purposeTo assess the utility of signal intensity ratio (SIR) in distinguishing between mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), thereby reducing unnecessary pancreatectomies or delayed diagnosis brought by misdiagnosis. MATERIALS AND

methodsThis retrospective study included 170 participants (34 with MFCP and 136 with PDAC) who underwent radical pancreatic surgery and were diagnosed via specimen pathology. The study group was carefully selected with a 1:4 ratio matching for sex, age, and operation time between two entities. T1 SIR, T2 SIR, arterial phase (AP) SIR, portal venous phase (VP) SIR, delay phase (DP) SIR, DWI

resultsThe ICC demonstrated excellent correlation for both intraobserver and interobserver(ICCs > 0.8). T1 SIR, AP SIR, VP SIR, and DP SIR were significantly lower in the PDAC group compared to the MFCP group, and exhibited good independent predictive properties with the sensitivities of 61.8, 61.8, 70.6, and 73.5%, specificities of 66.2, 68.4, 59.6, and 55.9%, and AUCs of 0.620, 0.659, 0.670, and 0.668, respectively, hovering around 0.7. The tenfold cross-validation confirmed the reliability and robustness of our findings, with consistent AUC, sensitivity, specificity, and 95% confidence intervals over 1000 iterations.

conclusionT1 SIR, AP SIR, VP SIR, and DP SIR show promise as potential imaging biomarkers for distinguishing between MFCP and PDAC.

Indexed as

Carcinoma, Pancreatic DuctalMagnetic Resonance ImagingPancreatic NeoplasmsPancreatitis, ChronicAdultAgedContrast MediaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedPreoperative CareReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityContrast MediaDifferential diagnosisMass-forming chronic pancreatitisMRIPancreatic ductal adenocarcinomaSignal intensity ratio

Identifiers

PMID39488674

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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.