Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study
Abstract Background 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 c...
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BMC
2025-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-13529-x |
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| author | Rongxue Shan Chenhao Pei Qianrui Fan Junchuan Liu Dawei Wang Shifeng Yang Ximing Wang |
| author_facet | Rongxue Shan Chenhao Pei Qianrui Fan Junchuan Liu Dawei Wang Shifeng Yang Ximing Wang |
| author_sort | Rongxue Shan |
| collection | DOAJ |
| description | Abstract Background 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. Purpose To evaluate the detection performance of an artificial intelligence (AI)-assisted platform for HCC by the external validation dataset. Methods 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. Results 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. Conclusion 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. |
| format | Article |
| id | doaj-art-d2e16992c5dc49ba9a1cb965de6469b8 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-d2e16992c5dc49ba9a1cb965de6469b82025-02-02T12:28:49ZengBMCBMC Cancer1471-24072025-01-0125111310.1186/s12885-025-13529-xArtificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation studyRongxue Shan0Chenhao Pei1Qianrui Fan2Junchuan Liu3Dawei Wang4Shifeng Yang5Ximing Wang6 Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityInstitute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International CenterInstitute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International CenterDepartment of Interventional Medicine, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital)Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityAbstract Background 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. Purpose To evaluate the detection performance of an artificial intelligence (AI)-assisted platform for HCC by the external validation dataset. Methods 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. Results 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. Conclusion 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.https://doi.org/10.1186/s12885-025-13529-xhepatocellular carcinomaArtificial intelligenceDeep learningSegmentationDetectionMulti-phase CT |
| spellingShingle | Rongxue Shan Chenhao Pei Qianrui Fan Junchuan Liu Dawei Wang Shifeng Yang Ximing Wang Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study BMC Cancer hepatocellular carcinoma Artificial intelligence Deep learning Segmentation Detection Multi-phase CT |
| title | Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study |
| title_full | Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study |
| title_fullStr | Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study |
| title_full_unstemmed | Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study |
| title_short | Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study |
| title_sort | artificial intelligence assisted platform performs high detection ability of hepatocellular carcinoma in ct images an external clinical validation study |
| topic | hepatocellular carcinoma Artificial intelligence Deep learning Segmentation Detection Multi-phase CT |
| url | https://doi.org/10.1186/s12885-025-13529-x |
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