CT texture analysis and CT features are predictive of PNET aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection.
CT is the preferred imaging modality for assessment of pancreatic diseases. Recent advances in CT (dual-energy CT, CT perfusion, CT volumetry, and radiogenomics) and emerging computational algorithms (machine learning) have the potential to further increase the value of CT in pancreatic imaging.
CT texture analysis and CT attenuation values allow reliable differentiation between neoplastic and bland thrombi on a single portal venous phase CT examination.
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