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An Orthotopic Resectional Mouse Model of Pancreatic Cancer
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Synoptic Multidisciplinary Team Meeting Workflows to Promote Guideline-Based Classification of Resectability in
William McGahan1,2,3, Nick Butler2,3, Thomas O'Rourke2,3
1Department of General Surgery, Royal Brisbane and Women's Hospital, Herston, QLD, Australia.
JCO Clinical Cancer Informatics
|January 6, 2026
Summary
A new web platform improved pancreatic cancer resectability classification by enhancing data capture for multidisciplinary team meetings (MDTMs). This system reduced unknown resectability and highlighted biologic factors over anatomy for survival outcomes.
Area of Science:
- Oncology
- Medical Informatics
- Surgical Oncology
Background:
- Pancreatic cancer resectability classification is often incomplete and inconsistent.
- The International Association of Pancreatology (IAP) provides anatomic, biologic, and conditional criteria for classification.
- Standardization is needed to improve patient management and outcomes.
Purpose of the Study:
- To address inconsistencies in pancreatic cancer resectability classification.
- To develop and evaluate a web-based platform for structured data capture and algorithm-driven classification.
- To integrate this platform into multidisciplinary team meeting (MDTM) workflows.
Main Methods:
- Designed and implemented an interoperable, web-based platform for structured pretreatment data capture.
- Linked modules supported MDTM referrals and discussions at two quaternary hospitals.
- Conducted a pre-post study comparing data completeness, resectability distribution, and treatment intent using statistical analyses (Pearson χ², logistic regression).
- Evaluated overall survival (OS) and impact of resectability criteria using Kaplan-Meier curves, log-rank tests, and Cox models.
Main Results:
- The platform improved documentation of tumor-vessel relationships, lymphadenopathy, and performance status (PS).
- It significantly reduced the proportion of patients with unknown resectability.
- Performance status ≥ 2 and elevated serum CA19.9 were associated with poorer overall survival, while anatomic criteria were not.
Conclusions:
- A synoptic intervention integrated into MDTM workflows enhances structured data capture for pancreatic cancer.
- The system effectively reduces unknown resectability classifications.
- Biologic and conditional criteria appear more relevant than purely anatomic factors for predicting survival in pancreatic cancer.
