A systematic review of comparisons of AI and radiologists in the diagnosis of HCC in multiphase CT: implications for practice.

IF 2.1 4区医学

Japanese Journal of Radiology Pub Date : 2025-08-18 DOI:10.1007/s11604-025-01853-y

Jarrod Younger, Emily Morris, Nicholas Arnold, Chanchala Athulathmudali, Janani Pinidiyapathirage, William MacAskill

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引用次数: 0

Abstract

Purpose: This systematic review aims to examine the literature of artificial intelligence (AI) algorithms in the diagnosis of hepatocellular carcinoma (HCC) among focal liver lesions compared to radiologists on multiphase CT images, focusing on performance metrics that include sensitivity and specificity as a minimum.

Methods: We searched Embase, PubMed and Web of Science for studies published from January 2018 to May 2024. Eligible studies evaluated AI algorithms for diagnosing HCC using multiphase CT, with radiologist interpretation as a comparator. The performance of AI models and radiologists was recorded using sensitivity and specificity from each study. TRIPOD + AI was used for quality appraisal and PROBAST was used to assess the risk of bias.

Results: Seven studies out of the 3532 reviewed were included in the review. All seven studies analysed the performance of AI models and radiologists. Two studies additionally assessed performance with and without supplementary clinical information to assist the AI model in diagnosis. Three studies additionally evaluated the performance of radiologists with assistance of the AI algorithm in diagnosis. The AI algorithms demonstrated a sensitivity ranging from 63.0 to 98.6% and a specificity of 82.0-98.6%. In comparison, junior radiologists (with less than 10 years of experience) exhibited a sensitivity of 41.2-92.0% and a specificity of 72.2-100%, while senior radiologists (with more than 10 years of experience) achieved a sensitivity between 63.9% and 93.7% and a specificity ranging from 71.9 to 99.9%.

Conclusion: AI algorithms demonstrate adequate performance in the diagnosis of HCC from focal liver lesions on multiphase CT images. Across geographic settings, AI could help streamline workflows and improve access to timely diagnosis. However, thoughtful implementation strategies are still needed to mitigate bias and overreliance.

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比较人工智能和放射科医生在多期CT诊断HCC的系统综述：对实践的意义。

目的：本系统综述旨在研究人工智能（AI）算法在局灶性肝病变中诊断肝细胞癌（HCC）的文献，并将其与放射科医生在多期CT图像上的诊断进行比较，重点关注包括敏感性和特异性在内的性能指标。方法：检索Embase、PubMed和Web of Science，检索2018年1月至2024年5月发表的研究。符合条件的研究评估了使用多期CT诊断HCC的人工智能算法，并以放射科医生的解释作为比较。使用每项研究的敏感性和特异性记录人工智能模型和放射科医生的表现。使用TRIPOD + AI进行质量评价，使用PROBAST评估偏倚风险。结果：3532项研究中有7项纳入了本次综述。所有七项研究都分析了人工智能模型和放射科医生的表现。另外两项研究评估了有无补充临床信息的表现，以协助人工智能模型进行诊断。三项研究还评估了放射科医生在人工智能算法帮助下的诊断表现。人工智能算法的灵敏度为63.0 ~ 98.6%，特异性为82.0 ~ 98.6%。相比之下，初级放射科医生（经验不足10年）的敏感性为41.2-92.0%，特异性为72.2-100%，而高级放射科医生（经验超过10年）的敏感性为63.9% - 93.7%，特异性为71.9 - 99.9%。结论：人工智能算法在肝局灶性病变的多期CT图像诊断HCC方面表现良好。在不同的地理环境中，人工智能可以帮助简化工作流程并改善获得及时诊断的机会。然而，仍然需要深思熟虑的实施策略来减轻偏见和过度依赖。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Japanese Journal of Radiology Medicine-Radiology, Nuclear Medicine and Imaging

自引率

4.80%

发文量

133

期刊介绍： Japanese Journal of Radiology is a peer-reviewed journal, officially published by the Japan Radiological Society. The main purpose of the journal is to provide a forum for the publication of papers documenting recent advances and new developments in the field of radiology in medicine and biology. The scope of Japanese Journal of Radiology encompasses but is not restricted to diagnostic radiology, interventional radiology, radiation oncology, nuclear medicine, radiation physics, and radiation biology. Additionally, the journal covers technical and industrial innovations. The journal welcomes original articles, technical notes, review articles, pictorial essays and letters to the editor. The journal also provides announcements from the boards and the committees of the society. Membership in the Japan Radiological Society is not a prerequisite for submission. Contributions are welcomed from all parts of the world.