Differentiating Ductal Adenocarcinoma of the Pancreas from Benign Conditions Using Routine Health Records: A Prospective Case-Control Study

Abstract

The study aimed to develop a prediction model for differentiating suspected PDAC from benign conditions. We used a prospective cohort of patients with pancreatic disease (n = 762) enrolled at the Barts Pancreas Tissue Bank (2008-2021) and performed a case-control study examining the association of PDAC (n = 340) with predictor variables including demographics, comorbidities, lifestyle factors, presenting symptoms and commonly performed blood tests. Age (over 55), weight loss in hypertensive patients, recent symptoms of jaundice, high serum bilirubin, low serum creatinine, high serum alkaline phosphatase, low red blood cell count and low serum sodium were identified as the most important features. These predictors were then used for training several machine-learning-based risk-prediction models on 75% of the cohort. Models were assessed on the remaining 25%. A logistic regression-based model had the best overall performance in the validation cohort (area-under-the-curve = 0.90; Spiegelhalter’s z = −1·82, p = 0.07). Setting a probability threshold of 0.15 guided by the maximum F2-score of 0.855, 96.8% sensitivity was reached in the full cohort, which could lead to earlier detection of 84.7% of the PDAC patients. The prediction model has the potential to be applied in primary, secondary and emergency care settings for the early distinction of suspected PDAC patients and expedited referral to specialist hepato-pancreatico-biliary services.

A central research portal for mining pancreatic clinical and molecular datasets and accessing biobanked samples

“…Under PCRFTB Data, PED lists the details of any returned data and associated sample characteristics available for analysis, while PCRFTB Research Projects links directly to the relevant published report. So far, this has further enriched the data available for banked tissue sample and/or patients, and currently includes data on stromal 40 , urinary miRNA 41 , metallomic 42 , and volatile organic compound 43 biomarkers, proteomics (ELISA) 44 , circulating tumour cells (CTCs) and xenotransplantation models 45,46 , germline and somatic mutations 47 , a phase I clinical trial 48 , and risk 49 and recurrence 50 predictions incorporating electronic health record data. To date, PCRFTB has processed 38 EoI, supported 33 research projects and 19 peer-reviewed publications.…”