Symptom network signatures for the early recognition of pancreatic cancer

BackgroundPancreatic cancer is a leading cause of cancer mortality, and early recognition is challenging. To achieve early diagnosis using symptoms alone, we examined patterns across different stages using network analysis to derive clinically useful insights. MethodsSymptom variables from a de-identified dataset of 50,000 pancreatic cancer patients were analyzed. Stratification by stage was done, followed by bootstrap resampling to address imbalances across strata. Symptom networks were then constructed with nodes representing symptoms and edges representing conditional dependencies estimated via an Ising-style neighborhood selection approach implemented through L1-regularized logistic regression. Strength, betweenness, and closeness centrality indices were then calculated, and their stability was analyzed using the case-dropping bootstrap. Network comparison tests were done, and difference networks were analyzed. Spring-layout algorithm was used for visualization, with node size being the predictability (pseudo-R{superscript 2}), and the edge weight being the mean partial correlation magnitude. ResultsOn average, symptoms were present in about one out of four patients (M = 0.26). Weight loss and abdominal discomfort were the most prevalent of the symptoms, followed by jaundice and back pain. Network structures became sparser across stages with a decreasing number of edges and centrality indices. Jaundice emerged as the dominant hub in Stage I, but shared dominance with Weight Loss in Stage II. Node predictability (pseudo-R2) was effectively zero across all disease stages. ConclusionOur network analysis of pancreatic cancer symptomatology across stages revealed distinct patterns that may improve understanding of its clinical presentation and support earlier recognition.