Opportunistic Detection of Hepatocellular Carcinoma Using Noncontrast CT and Deep Learning Artificial Intelligence

Hepatocellular carcinoma (HCC) poses a heavy global disease burden; early diagnosis is critical to improve outcomes. Opportunistic screening-the use of imaging data acquired for other clinical indications for disease detection-as well as the role of noncontrast CT have been poorly investigated in the context of HCC. We aimed to develop an artificial intelligence algorithm for efficient and accurate HCC detection using solely noncontrast CTs.

A 3-D convolutional block attention module (CABM) model was developed and trained on noncontrast multiphasic CT scans. HCC was diagnosed following American Association for the Study of Liver Disease guidelines and confirmed via 12-month clinical composite reference standard. CT observations were reviewed by radiologists; observations in at-risk patients were annotated via the Liver Imaging Reporting and Data System. Internal validation, independent external testing, and sensitivity analyses were performed to evaluate model performance and generalizability.

In all, 2,223 patients were included. The CBAM model achieved an area under the receiver operating curve (AUC) of 0.807 (95% confidence interval [CI] 0.772-0.841) on the internal validation cohort, comparable to radiological interpretation at 0.851 (95% CI 0.820-0.882). Among at-risk patients, cases with definite HCC outcomes, indeterminate scans, and scans with small lesions < 2 cm in size, the model attained AUCs of 0.769 (95% CI 0.721-0.817), 0.815 (95% CI 0.778-0.853), 0.769 (95% CI 0.704-0.834), and 0.773 (95% CI 0.692-0.854). On external testing cohort with 584 patients, the CBAM model achieved an AUC of 0.789 (95% CI 0.750-0.827).

The CBAM model achieved a diagnostic accuracy comparable to radiological interpretation during internal validation. Artificial intelligence analysis of noncontrast CTs has a potential role in HCC opportunistic screening.