An integrated CT/MRI diagnostic system for pancreatic cystic lesions that supports pathological classification and risk stratification.
Authors
Affiliations (17)
Affiliations (17)
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
- Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
- Pancreas Center, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
- Queen Mary School Hainan, Beijing University of Posts and Telecommunications, Lingshui 570203, China.
- Department of Radiology, First Affiliated Hospital of Hainan Medical University, Haikou 570102, China.
- Center for Health Statistics and Information, National Health Commission of the PRC, Beijing 100044, China.
- School of Public Health, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
- Department of Medical Imaging, Jinan Third People's Hospital, Jinan 250101, China.
- Department of Gastrointestinal Cancer Biology, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
- Department of Radiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China.
- Department of Radiology, Tianjin NanKai Hospital, Tianjin Medical University, Tianjin 300100, China.
- Department of Radiology, Tianjin First Central Hospital, Tianjin 300192, China.
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China.
- Department of Pathology, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
- Department of Eye and Vision Science, University of Liverpool, United Kingdom, Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool L7 8TX, United Kingdom.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong SAR, China.
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9713 GZ, The Netherlands.
Abstract
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. 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 (<i>n</i> = 858) and a temporal validation dataset (<i>n</i> = 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. 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% <i>vs</i>. 23.1% and over-examinations 6.7% <i>vs</i> 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. SIRS-PCL is a pathology-based, quantitative decision-support system for preoperative pathological subtype classification and pathological risk stratification of surgically evaluated neoplastic PCLs.