Study Design
Protocol overview
PREDICT-PanC is a prospective multicentre UK study evaluating a validated deep learning algorithm that analyses pre-MDT CT scans to classify pancreatic cancer resectability according to NCCN criteria (resectable / borderline resectable / locally advanced).
Phase A
Prospective Blinded Diagnostic Accuracy
The AI algorithm runs silently alongside standard MDT assessment. Clinical teams are blinded to AI outputs. Predictions are compared against MDT decisions and intraoperative / histopathological findings to establish diagnostic accuracy in a prospective real-world setting.
Phase B
Pragmatic Implementation Study
AI outputs are made available to MDTs as decision-support information prior to resectability discussion. The study evaluates whether AI-augmented MDT decision-making improves correct treatment allocation, reduces futile laparotomy rates, and affects time-to-treatment decision.
Primary Endpoint
1°
Correct classification of resectability status against intraoperative findings and histopathology (diagnostic accuracy of AI vs standard MDT assessment)
Key Secondary Endpoints
2°Futile laparotomy rate (exploratory laparotomy without resection)
2°R0 resection rate in patients proceeding to surgery
2°90-day morbidity assessed by Clavien-Dindo classification and Comprehensive Complication Index (CCI)
2°Time-to-treatment decision from MDT discussion
2°Health-economic analysis: cost per futile laparotomy avoided
Regulatory & Governance
Ethics, approvals & data governance
The prospective study has not yet commenced. All required approvals will be in place before data collection begins at any site.
NHS REC Ethics
Full NHS Research Ethics Committee submission in preparation. HRA approval required prior to site initiation.
Data Governance
All CT scans and clinical data fully anonymised. No personal identifiers in the research dataset. UK GDPR and NHS Code of Confidentiality compliant.
Portfolio Manager
Initial contact established with the NIHR CRN portfolio manager at the lead site. Portfolio registration planned prior to Phase A initiation.
NHS R&D
Trust R&D office engagement ongoing at the lead site. Site-specific assessments will be required at each participating centre.
Swiss Ethics (Retrospective)
Approved by the cantonal ethics committee. BASEC-ID: 2021-00457. Covers the retrospective algorithm development and validation cohort.
Sponsor
Sponsorship arrangements under discussion with the Trust Research Office at the lead site.
Project Timeline
From algorithm to clinical trial
A multi-year trajectory from retrospective model development to prospective clinical evaluation in NHS hospitals.
NOW
2022
Algorithm development
Model architecture designed at University of Basel. Training on retrospective CT data.
2024
Clinical collaboration
Partnership formed between Royal Free London, UCL, and University of Basel.
2026
Ethics & regulatory
IRAS submission, HRA approval, sponsor confirmation, site setup.
2028
Phase 1 analysis
Interim analysis. Transition to Phase 2 if concordance thresholds met.
2023
Retrospective validation
159-patient cohort. AUC 0.86. Swiss ethics approved (BASEC-ID: 2021-00457).
2025
Peer-reviewed publication
Paper presented at MIDL 2026. Protocol development and PPIE initiated.
H2 2027
Phase 1 recruitment
Observational phase. AI predictions collected alongside standard MDT decisions.
2029
Phase 2 — MDT integration
AI predictions presented live during MDT discussions. Impact evaluated prospectively.
Site Participation
Study sites & participation criteria
PREDICT-PanC will recruit from established UK HPB centres managing a sufficient volume of pancreatic cancer MDT cases. We are actively seeking expressions of interest from sites wishing to participate.
Royal Free London NHS Foundation Trust
Lead site · London · HPB & Liver Transplant Surgery
Lead Site
Additional UK HPB Centres
Multicentre recruitment — expressions of interest welcome
Sites TBC
If your centre manages pancreatic cancer MDT discussions and you are interested in site participation, please contact the study team. No commitment required at this stage.
Site participation criteria
Designated HPB or upper GI cancer centre with a functioning pancreatic cancer MDT
Minimum case volume: approximately 20+ pancreatic cancer MDT discussions per year
Access to PACS system and ability to export anonymised DICOM CT data
Named site principal investigator with HPB or oncology background
Local R&D office capacity to support site set-up and regulatory approvals
Commitment to prospective data capture and follow-up at defined timepoints
Investigator Team
Research team
The study brings together expertise in HPB surgery, clinical AI, biomedical engineering, radiology, anaesthetics, and biostatistics across Royal Free London, UCL, and the University of Basel.
SS
Mr Sebastian Staubli, MD, Dr. habil.
Principal Investigator
Senior Clinical Fellow, HPB & Liver Transplant Surgery · Royal Free London NHS FT · Honorary Research Associate, UCL
JP
Prof Joerg-Matthias Pollok, MD, PhD, FRCS
HPB & Liver Transplant Surgery
Consultant HPB & Liver Transplant Surgeon · Clinical Lead for HPB Surgery & Liver Transplantation · Royal Free London NHS FT · Professor of Surgery, UCL
PC
Prof Philippe C. Cattin, PhD
Medical Image Analysis & Navigation
Head, Center for medical Image Analysis & Navigation (CIAN) · Head, Dept. Biomedical Engineering · University of Basel
VO
Vincent Ochs
Algorithm Development
PhD Candidate · Center for medical Image Analysis & Navigation (CIAN) · Dept. Biomedical Engineering · University of Basel
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Study status: This website provides information about a research study currently under development. The study is being prepared for funding applications and ethics review, and has not yet begun recruiting participants. Information on this page is provided for transparency and to support patient and public involvement.
