A Causality-Informed Graph Intervention Model for Pancreatic Cancer Early Diagnosis

Pancreatic cancer is a highly fatal cancer type. Patients are typically in an advanced stage at their first diagnosis, mainly due to the absence of distinctive early-stage symptoms and lack of effective early diagnostic methods. In this work, we propose an automated method for pancreatic cancer diagnosis using non-contrast CT, taking advantage of its widespread availability in clinic. Currently, a primary challenge limiting the clinical value of intelligent systems is low generalization, i.e., the difficulty of achieving stable performance across datasets from different medical sources. To address this challenge, a novel causality-informed graph intervention model is developed based on a multi-instance-learning framework integrated with graph neural network for the extraction of local discriminative features. Within this model, we develop a graph causal intervention scheme with three levels of intervention for graph nodes, structures, and representations. This scheme systematically suppresses non-causal factors and thus lead to generalizable predictions. Specifically, first, a target node perturbation strategy is designed to capture target-region features. Second, a causal-structure separation module is developed to automatically identify the causal graph structures for obtaining stable representations of whole target regions. Third, a graph-level feature consistency mechanism is proposed to extract invariant features. Comprehensive experiments on large-scale datasets validated the promising early-diagnosis performance of our proposed model. The model generalizability was confirmed on three independent datasets, where the classification accuracy reached 86.3%, 80.4% and 82.2%, respectively. Overall, we provide a valuable potential tool for pancreatic cancer screening and early diagnosis. Our source codes will be released at https://github.com/SJTUBME-QianLab/GraphIntervention-PC .