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

IF 4 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of the American College of Radiology Pub Date : 2025-03-01 DOI:10.1016/j.jacr.2024.12.011
Chengzhi Peng MBBS , Philip Leung Ho Yu PhD , Jianliang Lu MPhil , Ho Ming Cheng PhD , Xin-Ping Shen MD , Keith Wan-Hang Chiu MD , Wai-Kay Seto MD
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引用次数: 0

Abstract

Objective

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.

Methods

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.

Results

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).

Discussion

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.
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来源期刊
Journal of the American College of Radiology
Journal of the American College of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
6.30
自引率
8.90%
发文量
312
审稿时长
34 days
期刊介绍: The official journal of the American College of Radiology, JACR informs its readers of timely, pertinent, and important topics affecting the practice of diagnostic radiologists, interventional radiologists, medical physicists, and radiation oncologists. In so doing, JACR improves their practices and helps optimize their role in the health care system. By providing a forum for informative, well-written articles on health policy, clinical practice, practice management, data science, and education, JACR engages readers in a dialogue that ultimately benefits patient care.
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