Abstract
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–validationtesting 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. Copyright © 2024 The Author(s).
Methods: We developed four deep learning models for diagnosing HCC on computed tomography (CT) via a training–validationtesting 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. Copyright © 2024 The Author(s).
| Original language | English |
|---|---|
| Article number | 101219 |
| Journal | JHEP Reports |
| Volume | 7 |
| Early online date | Sept 2024 |
| DOIs | |
| Publication status | Published - 2025 |
Citation
Yu, P. L. H., Chiu, K. W.-H., Lu, J., Lui, G. C. S., Zhou, J., Cheng, H.-M., Mao, X., Wu, J., Shen, X.-P., Kwok, K. M., Kan, W. K., Ho, Y. C., Chan, H. T., Xiao, P., Mak, L.-Y., Tsui, V. W. M., Hui, C., Lam, P. M., Deng, Z., ... Seto, W.-K. (2025). Application of a deep learning algorithm for the diagnosis of HCC. JHEP Reports, 7, Article 101219. https://doi.org/10.1016/j.jhepr.2024.101219Keywords
- HCC
- AI
- Liver cancer
- CT
- LIRADS
- Imaging