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Hepatocellular carcinoma (HCC) can be potentially discovered from abdominalcomputed tomography (CT) studies under varied clinical scenarios, e.g., fullydynamic contrast enhanced (DCE) studies, non-contrast (NC) plus venous phase(VP) abdominal studies, or NC-only studies. We develop a flexiblethree-dimensional deep algorithm, called hetero-phase volumetric detection(HPVD), that can accept any combination of contrast-phase inputs and withadjustable sensitivity depending on the clinical purpose. We trained HPVD on771 DCE CT scans to detect HCCs and tested on external 164 positives and 206controls, respectively. We compare performance against six clinical readers,including two radiologists, two hepato-pancreatico-biliary (HPB) surgeons, andtwo hepatologists. The area under curve (AUC) of the localization receiveroperating characteristic (LROC) for NC-only, NC plus VP, and full DCE CTyielded 0.71, 0.81, 0.89 respectively. At a high sensitivity operating point of80% on DCE CT, HPVD achieved 97% specificity, which is comparable to measuredphysician performance. We also demonstrate performance improvements over moretypical and less flexible non hetero-phase detectors. Thus, we demonstrate thata single deep learning algorithm can be effectively applied to diverse HCCdetection clinical scenarios.

A Flexible Three-Dimensional Hetero-phase Computed Tomography  Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and  Practical HCC Screening