Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma
ObjectivesTo explore the utility of machine learning-based ultrasound radiomics for predicting TP53 gene mutation in hepatocellular carcinoma (HCC).Methods154 HCC patients with 182 lesions from 2019 to 2024 were reviewed retrospectively. All lesions were randomly split into the training set (n = 129...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1565618/full |
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| author | Didi Bu Shaobo Duan Shanshan Ren Yujing Ma Yuanyuan Liu Yahong Li Xiguo Cai Lianzhong Zhang Lianzhong Zhang Lianzhong Zhang |
| author_facet | Didi Bu Shaobo Duan Shanshan Ren Yujing Ma Yuanyuan Liu Yahong Li Xiguo Cai Lianzhong Zhang Lianzhong Zhang Lianzhong Zhang |
| author_sort | Didi Bu |
| collection | DOAJ |
| description | ObjectivesTo explore the utility of machine learning-based ultrasound radiomics for predicting TP53 gene mutation in hepatocellular carcinoma (HCC).Methods154 HCC patients with 182 lesions from 2019 to 2024 were reviewed retrospectively. All lesions were randomly split into the training set (n = 129) and the test set (n = 53), and ultrasound radiomics features were extracted and selected. Extreme gradient boosting tree (XGBoost), decision tree (DT), random forest (RF), support vector machine (SVM), and logistic regression (LR) were used to construct the ultrasound radiomics models, the clinical models, and the combined models. The predictive performance of various models was evaluated by the area under the curve (AUC), accuracy, calibration curve, and decision curve analysis (DCA).ResultsAmong the 182 lesions, 102 were confirmed as mutant TP53 and 80 were confirmed as wild-type TP53. The ultrasound radiomics model obtained an AUC of 0.778 and an accuracy of 0.774 in the test set. The clinical model achieved an AUC of 0.761 and an accuracy of 0.710 in the test set. Notably, integrating clinical features with ultrasound radiomics further enhanced predictive performance. The XGBoost-based combined model exhibited the highest predictive performance among all models, achieving an AUC of 0.846 and an accuracy of 0.823 in the test set. The decision curve analysis and calibration curve revealed that the XGBoost-based combined model provided the highest clinical benefit and exhibited strong predictive consistency.ConclusionMachine learning-based ultrasound radiomics signatures accurately predict TP53 gene mutations in HCC. The XGBoost-based combined model, which combined ultrasound radiomics features with clinical features, showed the best performance and represented a promising noninvasive approach for screening TP53-mutated HCC. |
| format | Article |
| id | doaj-art-a5ec96f8fe264342aad2dea5ab7f6485 |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-a5ec96f8fe264342aad2dea5ab7f64852025-08-20T02:29:46ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-04-011210.3389/fmed.2025.15656181565618Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinomaDidi Bu0Shaobo Duan1Shanshan Ren2Yujing Ma3Yuanyuan Liu4Yahong Li5Xiguo Cai6Lianzhong Zhang7Lianzhong Zhang8Lianzhong Zhang9Department of Ultrasound, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University, Zhengzhou, ChinaDepartment of Health Management, Henan Provincial People’s Hospital, Zhengzhou, ChinaDepartment of Ultrasound, Henan Provincial People’s Hospital, Zhengzhou, ChinaDepartment of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, ChinaDepartment of Ultrasound, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University, Zhengzhou, ChinaDepartment of Ultrasound, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University, Zhengzhou, ChinaHenan Rehabilitation Clinical Medical Research Center, Henan Provincial People’s Hospital, Zhengzhou, ChinaDepartment of Ultrasound, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University, Zhengzhou, ChinaHenan Rehabilitation Clinical Medical Research Center, Henan Provincial People’s Hospital, Zhengzhou, ChinaHenan Key Laboratory for Ultrasound Molecular Imaging and Artificial Intelligence Medicine, Henan Provincial People’s Hospital, Zhengzhou, ChinaObjectivesTo explore the utility of machine learning-based ultrasound radiomics for predicting TP53 gene mutation in hepatocellular carcinoma (HCC).Methods154 HCC patients with 182 lesions from 2019 to 2024 were reviewed retrospectively. All lesions were randomly split into the training set (n = 129) and the test set (n = 53), and ultrasound radiomics features were extracted and selected. Extreme gradient boosting tree (XGBoost), decision tree (DT), random forest (RF), support vector machine (SVM), and logistic regression (LR) were used to construct the ultrasound radiomics models, the clinical models, and the combined models. The predictive performance of various models was evaluated by the area under the curve (AUC), accuracy, calibration curve, and decision curve analysis (DCA).ResultsAmong the 182 lesions, 102 were confirmed as mutant TP53 and 80 were confirmed as wild-type TP53. The ultrasound radiomics model obtained an AUC of 0.778 and an accuracy of 0.774 in the test set. The clinical model achieved an AUC of 0.761 and an accuracy of 0.710 in the test set. Notably, integrating clinical features with ultrasound radiomics further enhanced predictive performance. The XGBoost-based combined model exhibited the highest predictive performance among all models, achieving an AUC of 0.846 and an accuracy of 0.823 in the test set. The decision curve analysis and calibration curve revealed that the XGBoost-based combined model provided the highest clinical benefit and exhibited strong predictive consistency.ConclusionMachine learning-based ultrasound radiomics signatures accurately predict TP53 gene mutations in HCC. The XGBoost-based combined model, which combined ultrasound radiomics features with clinical features, showed the best performance and represented a promising noninvasive approach for screening TP53-mutated HCC.https://www.frontiersin.org/articles/10.3389/fmed.2025.1565618/fullradiomicshepatocellular carcinomamachine learningTP53ultrasonography |
| spellingShingle | Didi Bu Shaobo Duan Shanshan Ren Yujing Ma Yuanyuan Liu Yahong Li Xiguo Cai Lianzhong Zhang Lianzhong Zhang Lianzhong Zhang Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma Frontiers in Medicine radiomics hepatocellular carcinoma machine learning TP53 ultrasonography |
| title | Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma |
| title_full | Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma |
| title_fullStr | Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma |
| title_full_unstemmed | Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma |
| title_short | Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma |
| title_sort | machine learning based ultrasound radiomics for predicting tp53 mutation status in hepatocellular carcinoma |
| topic | radiomics hepatocellular carcinoma machine learning TP53 ultrasonography |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1565618/full |
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