Shan Rongxue, Pei Chenhao, Fan Qianrui, Liu Junchuan, Wang Dawei, Yang Shifeng, Wang Ximing
Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China.
Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, 271099, China.
BMC Cancer. 2025 Jan 27;25(1):154. doi: 10.1186/s12885-025-13529-x.
Accurate detection of hepatocellular carcinoma (HCC) in multiphasic contrast CT is essential for effective treatment and surgical planning. However, the variety of CT images, the misdiagnosis and missed diagnosis, and the inconsistent diagnosis among different radiologists pose challenges to accurate detection which demands sufficient clinical experience and can be time-consuming and labor-intensive.
To evaluate the detection performance of an artificial intelligence (AI)-assisted platform for HCC by the external validation dataset.
CT images pathologically diagnosed with HCC from December 2021 to June 2023 were retrospectively analyzed to evaluate the detection ability of the AI-assisted platform. The AI-assisted platform is designed based on a two-phase segmentation approach, integrating coarse and fine segmentation techniques to accurately identify and delineate hepatic lesions. The CT images were annotated and confirmed by the experienced radiologists using InferScholar software as the "gold standard". The automatic HCC segmentation performed by the AI-assisted platform was used to compare with the annotation of radiologists. Furthermore, we also did subgroup analysis depending on the size and location of HCC to explore the impact factors of HCC detectability. The segmentation accuracies were evaluated by Dice coefficient (Dice), accuracy, recall, precision, and F1-score. Our study focused on evaluating the efficacy of the AI-assisted platform in clinical settings.
One Hundred Forty HCC patients were finally included in this study. The artificial intelligence (AI)-assisted platform's performance was rigorously assessed by comparing the segmentation outcomes with standard diagnostic criteria. The average dice score of the AI-assisted platform is 0.8819, which showed a high detection performance for HCC. Besides, for the subgroup analysis, the model also demonstrated high performance in diameter greater than 20 mm with all results exceeding 0.9, and all final evaluation index values for the location analysis were consistently exceeding 0.97. All the results showed comparable performance with radiologists. Our results demonstrate that the product not only accurately segments HCC lesions but also provides valuable insights into lesion characteristics that are essential for effective treatment planning.
This study validates the effectiveness of the artificial intelligence-assisted platform in detecting HCC lesions and analyzing lesion size and location. It can serve as an auxiliary tool to help radiologists identify, locate, and assess lesions.
在多期增强CT中准确检测肝细胞癌（HCC）对于有效治疗和手术规划至关重要。然而，CT图像的多样性、误诊和漏诊以及不同放射科医生之间诊断的不一致性给准确检测带来了挑战，这需要足够的临床经验，且可能耗时费力。
通过外部验证数据集评估人工智能（AI）辅助平台对HCC的检测性能。
回顾性分析2021年12月至2023年6月间经病理诊断为HCC的CT图像，以评估AI辅助平台的检测能力。该AI辅助平台基于两阶段分割方法设计，整合了粗分割和细分割技术，以准确识别和勾勒肝脏病变。CT图像由经验丰富的放射科医生使用InferScholar软件进行标注并确认为“金标准”。将AI辅助平台进行的自动HCC分割与放射科医生的标注进行比较。此外，我们还根据HCC的大小和位置进行了亚组分析，以探讨HCC可检测性的影响因素。通过Dice系数（Dice）、准确率、召回率、精确率和F1分数评估分割准确性。我们的研究重点是评估AI辅助平台在临床环境中的有效性。
本研究最终纳入了140例HCC患者。通过将分割结果与标准诊断标准进行比较，对人工智能（AI）辅助平台的性能进行了严格评估。AI辅助平台的平均Dice分数为0.8819，对HCC显示出较高的检测性能。此外，对于亚组分析，该模型在直径大于20mm时也表现出高性能，所有结果均超过0.9，位置分析的所有最终评估指标值均持续超过0.97。所有结果显示与放射科医生的表现相当。我们的数据表明，该产品不仅能准确分割HCC病变，还能提供对病变特征的有价值见解，这对有效的治疗规划至关重要。
本研究验证了人工智能辅助平台在检测HCC病变以及分析病变大小和位置方面的有效性。它可以作为一种辅助工具，帮助放射科医生识别、定位和评估病变。
