  Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal
computed tomography (CT) studies under varied clinical scenarios, e.g., fully
dynamic contrast enhanced (DCE) studies, non-contrast (NC) plus venous phase
(VP) abdominal studies, or NC-only studies. We develop a flexible
three-dimensional deep algorithm, called hetero-phase volumetric detection
(HPVD), that can accept any combination of contrast-phase inputs and with
adjustable sensitivity depending on the clinical purpose. We trained HPVD on
771 DCE CT scans to detect HCCs and tested on external 164 positives and 206
controls, respectively. We compare performance against six clinical readers,
including two radiologists, two hepato-pancreatico-biliary (HPB) surgeons, and
two hepatologists. The area under curve (AUC) of the localization receiver
operating characteristic (LROC) for NC-only, NC plus VP, and full DCE CT
yielded 0.71, 0.81, 0.89 respectively. At a high sensitivity operating point of
80% on DCE CT, HPVD achieved 97% specificity, which is comparable to measured
physician performance. We also demonstrate performance improvements over more
typical and less flexible non hetero-phase detectors. Thus, we demonstrate that
a single deep learning algorithm can be effectively applied to diverse HCC
detection clinical scenarios.
