ArticleBMC cancer2025
Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Real World Analysis of Imaging Compared to Biopsy for Diagnosis of Pediatric Liver Tumors.Pediatric gastroenterology, hepatology & nutrition · 2026Article
- Hepatocellular carcinoma surveillance: a health economic evaluation.Clinical and molecular hepatology · 2026Review
- Pediatric Personalized Deep Learning Models for Segmentation of Hepatoblastoma at CT and MRI.Radiology. Imaging cancer · 2026Article
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- Evaluating the clinical utility of large language models for hepatocellular carcinoma treatment recommendations: A nationwide retrospective registry study.PLoS medicine · 2026Article
- Integrating pretreatment CT radiomics and circulating tumor cells using machine learning to predict survival in hepatocellular carcinoma.Frontiers in pharmacology · 2026Article
- Dual-branch collaborative GAN with multi-scale CBAM and anatomical topology coding for enhanced early HCC detection in CT.Scientific reports · 2025Article
- Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion.Bioengineering (Basel, Switzerland) · 2025Article
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7 authors.
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Abstract
backgroundAccurate 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. PURPOSE: To evaluate the detection performance of an artificial intelligence (AI)-assisted platform for HCC by the external validation dataset.
methodsCT 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.
resultsOne 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.
conclusionThis 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.
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