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

• Published in: Cancers Vol. 15; no. 1; p. 280
• Main Authors: Zardab, Mohamed, Balarajah, Vickna, Banerjee, Abhirup, Stasinos, Konstantinos, Saad, Amina, Imrali, Ahmet, Hughes, Christine, Roberts, Rhiannon, Vajrala, Ajith, Chelala, Claude, Kocher, Hemant M, Dayem Ullah, Abu Z M
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31-12-2022; MDPI
• Subjects: Adenocarcinoma; Algorithms; Alkaline phosphatase; Benign; Bilirubin; Biomarkers; Body weight loss; Carcinoma, Ductal; Comorbidity; Constipation; Creatinine; Demography; Diabetes; Diagnosis; Diagnosis, Differential; Disease; Electronic health records; Emergency medical care; Erythrocytes; Histology; Jaundice; Laboratories; Learning algorithms; Lifestyles; Machine learning; Medical prognosis; Methods; Pancreas; Pancreatic cancer; Pancreatic diseases; Patients; Prediction models; Primary care; Tumors; United Kingdom; symptoms; blood test; PDAC; comorbidity; prediction model
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15010280

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 (
= 762) enrolled at the Barts Pancreas Tissue Bank (2008-2021) and performed a case-control study examining the association of PDAC (
= 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,
= 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.