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.

翻译：从不同临床情景下的腹部计算断层成像(CT)研究中可能发现肝细胞癌(HCC),例如,完全动态对比强化(DCE)研究、非盘旋(NC)和静脉阶段(VP)腹部研究,或NC专用研究。我们开发了一个灵活的三维深度算法,称为肝相体体积检测(HPVD),它可以接受任何对比阶段投入的组合,并且根据临床目的具有可调适的敏感性。我们在771DCE的临床透析中培训了HPVD,以检测HCCs(DCE)的771 DCE扫描,并分别测试外部164正数和206项控制。我们与6个临床读者,包括2个放射学家、2个肝脏-肝脏血压(HPB)外科医生和2个肝炎科医生进行了对比。在CUC(AUC)下区域,仅用于NC、NC+VP(LOC)的地方化接收器操作特性(LOC)和全DCCT(CT),分别检测到0.71、0.81、0.89和209个典型的DNA检测结果(HCR)分别检测结果,我们测量为80(HCS-Cretretreal),这也显示为不具有可比较性能性能性能性能度的80-CS)。