@article{Cui2026, 
author = {Xiaonan Cui and Houpu Liu and Sunyi Zheng and Hongwei Wang and Jiaxin Liu and Ziyi Gao and Tianye Liu and Jing Liang and Xiaoxu Wang and Xinru Zheng and Jing Wang and Keyi Bian and Xiaomeng Yang and Chunlin Zhang and Tianshuo Jiao and Yufei Yang and Shuxuan Fan and Xiuchao Wang and Song Gao and Chuntao Gao and Yukuan Feng and Hui Li and Guoping Xu and Xiang Zhang and Wen Shen and Haoran Sun and Baocun Sun and Yan Sun and Jianyu Xiao and Yalin Zheng and Michael Ying and Geertruida H. de Bock and Zhaoxiang Ye and Jihui Hao},
title = {An integrated CT/MRI diagnostic system for pancreatic cystic lesions that supports pathological classification and risk stratification},
year = {2026},
journal = {Cancer Biology & Medicine},
volume = {23},
number = {10},
pages = {1495-1512},
keywords = {Pancreatic cystic lesions, machine learning, abdominal imaging, noninvasive, cyst-related malignancy},
url = {https://www.sciopen.com/article/10.20892/j.issn.2095-3941.2026.0566},
doi = {10.20892/j.issn.2095-3941.2026.0566},
abstract = {ObjectiveTo 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.MethodsWe 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.ResultsA 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.ConclusionsSIRS-PCL is a pathology-based, quantitative decision-support system for preoperative pathological subtype classification and pathological risk stratification of surgically evaluated neoplastic PCLs.}
}