Improving risk prediction for pancreatic cancer in symptomatic patients: a Saudi Arabian study
Cancer Manag Res.
Ahmed AE1,2, Alzahrani FS2, Gharawi AM2, Alammary SA2, Almijmaj FH2, Alhusayni FM2, McClish DK3, Al-Jahdali H1,2, Olayan AAA4, Jazieh AR4.
1 King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia, email@example.com.
2 King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia, firstname.lastname@example.org.
3 Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
4 King Abdulaziz Medical City, National Guards Health Affairs, Riyadh, Saudi Arabia.
Year of Publication:
Imaging tests used in our center are usually inadequate to confirm the high risk for pancreatic cancer. We aimed to use a combination of potential predictors including imaging tests to quantify the risk of pancreatic cancer and evaluate its utility.
This was a retrospective cohort study of patients who were suspected as having pancreatic cancer and underwent biopsy examination of pancreatic mass at King Abdulaziz Medical City, Riyadh, Saudi Arabia, between January 1, 2013, and December 31, 2016. We retrieved data on demographics, clinical history, imaging tests, and final pancreatic diagnosis from medical records.
Of the 206 who underwent pancreatic biopsies, the mean age was 63.6 years; 54.4% were male. Of all the biopsies, 57.8% were malignant and 42.2% were benign masses. Nine factors contributed significantly to the risk of pancreatic cancer and were noted: older age (adjusted odds ratio [aOR] =1.048; P=0.010), male gender (aOR =4.670; P=0.008), weight loss (aOR =14.810; P=0.001), abdominal pain (aOR =7.053; P=0.0.001), blood clots (aOR =20.787; P=0.014), pancreatitis (aOR =4.473; P=0.021), jaundice (aOR =7.446; P=0.003), persistent fatigue (aOR =22.015; P=0.015), and abnormal imaging tests (aOR =67.124; P=0.001). The model yielded powerful calibration (P=0.953), excellent predictive utility (area under the receiver operating characteristic curve 96.3%; 95% CI =94.1, 98.6), with optimism-corrected area under the curve bootstrap resampling of 94.9%. An optimal cut-off risk probability of 0.513 yielded a sensitivity of 94% and specificity of 84.7% for risk classification.
The study developed and validated a risk model for quantifying the risk of pancreatic cancer. Nine characteristics were associated with increased risk of pancreatic cancer. This risk assessment model is feasible and highly sensitive and could be useful to improve screening performance and the decision-making process in clinical settings in Saudi Arabia.