Improving the Reliability of Medical Diagnostic Models through Rule-Based Decision Deferral

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, and accurate assessment of tumor resectability is crucial for determining appropriate treatment. AI-based models have shown promise in classifying tumor resectability, but reliability concerns have impeded clinical implementation. We propose extending the AI-based VasQNet model for classifying tumor resectability on AI-generated segmentations of computed tomography scans (CTs) to improve the models’ reliability. This extension allows VasQNet to defer decisions when the AI-generated segmentations violate pre-established rules on vascular anatomy, tumor location, and tumor size. We conducted experiments using CTs of (borderline) resectable and non-resectable PDAC patients. We evaluated the performance of the baseline VasQNet and the extended VasQNet with rule-based decision deferral (RBDD) by comparing their classifications to a ground-truth provided by a radiologist, employing agreement as a metric. Our results demonstrate that the extended VasQNet achieved a significantly higher agreement (90%) with the radiologist’s classification than the baseline VasQNet (67%). Notably, 17/31 (54%) deferred decisions would have been incorrect had they not been deferred. Our study demonstrates the effectiveness of RBDD in improving the reliability of clinical diagnostic models through the exemplification of VasQNet. In conclusion, RBDD can enhance the reliability of clinical diagnostics models, facilitating integration into clinical practice. The documented code is available on GitHub (https://github.com/PHAIR-Consortium/Vessel-Involvement-Quantifier).

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