Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stability and multi-institutional generalisability
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The authors aimed to develop and validate the Radiomics-based Early Detection MODel (REDMOD), an AI framework designed to detect visually occult pancreatic ductal adenocarcinoma (PDA) using standard CT imaging. In a multi-institutional study, REDMOD demonstrated significantly higher sensitivity for early PDA detection compared to radiologists, achieving an area under the curve (AUC) of 0.82 and maintaining strong longitudinal stability and generalizability across diverse datasets. These findings suggest that REDMOD could facilitate earlier diagnosis and intervention in high-risk populations, potentially improving survival outcomes.
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