An integrated CT/MRI diagnostic system for pancreatic cystic lesions that supports pathological classification and risk stratification
In brief
CT/MRI cyst tool nearly halves unnecessary pancreatic surgery in comparison
In a 1,102-patient retrospective study, the imaging-based system classified five pancreatic cyst types with 93% discrimination and was associated with fewer unnecessary surgeries than current practice: 11.5% versus 23.1%. In a prospective group of 76 patients, classification accuracy was 86.2%; the results apply to surgically evaluated cysts, and broader validation is still needed.
- Journal
- Cancer biology & medicine (Q1)
- Published
- 29 October 2026
- Study design
- Prospective / inception cohort
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Xiaonan Cui, Houpu Liu, Sunyi Zheng, Hongwei Wang, Jiaxin Liu, Ziyi Gao, et al.
- PMID
- 42972650
- DOI
- 10.20892/j.issn.2095-3941.2026.0566
Why clinicians should know about it
- Picked for Health Informatics (paper of the day, 4 November 2026): Decision-support system evaluated prospectively in clinical practice
Abstract
OBJECTIVE: To develop a pathology-based preoperative decision-support system (Structured Imaging Report System for Pancreatic Cystic Lesions, SIRS-PCL) for subtype classification and risk stratification of PCLs. METHODS: We conducted a multicentre retrospective study involving 1,102 patients treated between 1 January 2011 and 31 December 2024. The study cohort was divided into a development dataset (n = 858) and a temporal validation dataset (n = 244) from the periods 2011-2022 and 2023-2024, respectively. Three logistic prediction models were used to classify the five PCL subtypes and to perform separate three-tier risk stratifications (benign/low, medium, and high) for intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN) cases. SIRS-PCL was prospectively implemented at Tianjin Medical University Cancer Institute and Hospital from 25 June 2025 to 25 June 2026, and the diagnostic accuracy, sensitivity, and specificity were evaluated in a cohort of 76 patients. RESULTS: A total of 1,102 patients with PCLs were included in the study (median age 56 years, interquartile range 44-64), of whom 714 were female (64.8%). The five-category model included in SIRS-PCL achieved a mean area under the receiver operating characteristic curve (AUC) of 0.93 [95% confidence interval (CI) 0.91-0.95] in temporal validation. The IPMN model showed good discrimination for risk stratification (mean AUC 0.86, 95% CI 0.79-0.93), with the highest overall accuracy of 77.1% exceeding current guidelines (range 50.0%-58.6%). The MCN risk model also showed high performance (mean AUC 0.97, 95% CI 0.91-1.00). In comparison to current practice, SIRS-PCL use reduced unnecessary surgeries 11.5% vs. 23.1% and over-examinations 6.7% vs 33.7%. In the prospective implementation cohort, SIRS-PCL achieved accuracy of 86.2% for five-category pathological classification, 73.7% for IPMN risk stratification, and 100% for MCN risk stratification. CONCLUSIONS: SIRS-PCL is a pathology-based, quantitative decision-support system for preoperative pathological subtype classification and pathological risk stratification of surgically evaluated neoplastic PCLs.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.