Application of a deep learning algorithm for the diagnosis of HCC - Philip Leung Ho Yu; Keith Wan-Hang Chiu; Jianliang Lu; Gilbert C.S. Lui; Jian Zhou; Ho-Ming Cheng; Xianhua Mao; Juan Wu; Xin-Ping Shen; King Ming Kwok; Wai Kuen Kan; Y.C. Ho; et al. (JHEP Reports)
Background & Aims: Hepatocellular carcinoma (HCC) is characterized by a high mortality rate. The Liver Imaging Reporting and Data System (LI-RADS) results in a considerable number of indeterminate observations, rendering an accurate diagnosis difficult. Methods: We developed four deep learning models for diagnosing HCC on computed tomography (CT) via a training–validation–testing approach. Thin-slice triphasic CT liver images and relevant clinical information were collected and processed for deep learning. HCC was diagnosed and verified via a 12-month clinical composite reference standard. CT observations among at-risk patients were annotated using LI-RADS. Diagnostic performance was assessed by internal validation and independent external testing. We conducted sensitivity analyses of different subgroups, deep learning explainability evaluation, and misclassification analysis. Results: From 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under receiver-operating-characteristic curves (AUCs) of 0.919 (95% CI, 0.903–0.935) and 0.901 (95% CI, 0.879–0.924) at the observation (n = 1,077) and patient (n = 685) levels, respectively during internal validation, compared with 0.839 (95% CI, 0.814–0.864) and 0.822 (95% CI, 0.790–0.853), respectively for standard of care radiological interpretation. The negative predictive values of ST3DCN were 0.966 (95% CI, 0.954–0.979) and 0.951 (95% CI, 0.931–0.971), respectively. The observation-level AUCs among at-risk patients, 2–5-cm observations, and singular portovenous phase analysis of ST3DCN were 0.899 (95% CI, 0.874–0.924), 0.872 (95% CI, 0.838–0.909) and 0.912 (95% CI, 0.895–0.929), respectively. In external testing (551/717 patients/observations), the AUC of ST3DCN was 0.901 (95% CI, 0.877–0.924), which was non-inferior to radiological interpretation (AUC 0.900; 95% CI, 0.877–-923). Conclusions: ST3DCN achieved strong, robust performance for accurate HCC diagnosis on CT. Thus, deep learning can expedite and improve the process of diagnosing HCC. Impact and implications:: The clinical applicability of deep learning in HCC diagnosis is potentially huge, especially considering the expected increase in the incidence and mortality of HCC worldwide. Early diagnosis through deep learning can lead to earlier definitive management, particularly for at-risk patients. The model can be broadly deployed for patients undergoing a triphasic contrast CT scan of the liver to reduce the currently high mortality rate of HCC.