Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model
Objective: A prognostic model utilizing CT radiomics, radiological, and clinical features was developed and validated in this study to predict an objective response to initial transcatheter arterial chemoembolization with drug-eluting beads (DEB-TACE) for hepatocellular carcinoma (HCC).Methods: Betw...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1315732/full |
| _version_ | 1797347178841112576 |
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| author | Xueying Zhang Zijun He Yucong Zhang Jian Kong |
| author_facet | Xueying Zhang Zijun He Yucong Zhang Jian Kong |
| author_sort | Xueying Zhang |
| collection | DOAJ |
| description | Objective: A prognostic model utilizing CT radiomics, radiological, and clinical features was developed and validated in this study to predict an objective response to initial transcatheter arterial chemoembolization with drug-eluting beads (DEB-TACE) for hepatocellular carcinoma (HCC).Methods: Between January 2017 and December 2022, the baseline clinical characteristics and preoperative and postoperative follow-up imaging data of 108 HCC patients who underwent the first time treatment of DEB-TACE were analyzed retrospectively. The training group (n = 86) and the validation group (n = 22) were randomly assigned in an 8:2 ratio. By logistic regression in machine learning, radiomics, and clinical-radiological models were constructed separately. Finally, the integrated model construction involved the integration of both radiomics and clinical-radiological signatures. The study compared the integrated model with radiomics and clinical-radiological models using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).Results: The objective response rate observed in a group of 108 HCC patients who received initial DEB-TACE treatment was found to be 51.9%. Among the three models, the integrated model exhibited superior predictive accuracy in both the training and validation groups. The training group resulted in an area under the curve (AUC) of 0.860, along with sensitivity and specificity values of 0.650 and 0.913, respectively. Based on the findings from the validation group, the AUC was estimated to be 0.927. Additionally, it was found that values of sensitivity and specificity were 0.875 and 0.833, respectively. In the validation group, the AUC of the integrated model showed a significant improvement when contrasted to the clinical-radiological model (p = 0.042). Nevertheless, no significant distinction was observed in the AUC when comparing the integrated model with the radiomics model (p = 0.734). The DCA suggested that the integrated model demonstrates advantageous clinical utility.Conclusion: The integrated model, which combines the CT radiomics signature and the clinical-radiological signature, exhibited higher predictive efficacy than either the radiomics or clinical-radiological models alone. This suggests that during the prediction of the objective responsiveness of HCC patients to the first DEB-TACE treatment, the integrated model yields superior outcomes. |
| first_indexed | 2024-03-08T11:43:56Z |
| format | Article |
| id | doaj.art-e308fe340d7944cdbb06b521036637bd |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-03-08T11:43:56Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-e308fe340d7944cdbb06b521036637bd2024-01-25T04:14:29ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122024-01-011510.3389/fphar.2024.13157321315732Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning modelXueying Zhang0Zijun He1Yucong Zhang2Jian Kong3The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, ChinaThe Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, ChinaDepartment of Radiation Oncology, Shenzhen People’s Hospital (Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, Guangdong, ChinaDepartment of Interventional Radiology, Shenzhen People’s Hospital (Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, Guangdong, ChinaObjective: A prognostic model utilizing CT radiomics, radiological, and clinical features was developed and validated in this study to predict an objective response to initial transcatheter arterial chemoembolization with drug-eluting beads (DEB-TACE) for hepatocellular carcinoma (HCC).Methods: Between January 2017 and December 2022, the baseline clinical characteristics and preoperative and postoperative follow-up imaging data of 108 HCC patients who underwent the first time treatment of DEB-TACE were analyzed retrospectively. The training group (n = 86) and the validation group (n = 22) were randomly assigned in an 8:2 ratio. By logistic regression in machine learning, radiomics, and clinical-radiological models were constructed separately. Finally, the integrated model construction involved the integration of both radiomics and clinical-radiological signatures. The study compared the integrated model with radiomics and clinical-radiological models using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).Results: The objective response rate observed in a group of 108 HCC patients who received initial DEB-TACE treatment was found to be 51.9%. Among the three models, the integrated model exhibited superior predictive accuracy in both the training and validation groups. The training group resulted in an area under the curve (AUC) of 0.860, along with sensitivity and specificity values of 0.650 and 0.913, respectively. Based on the findings from the validation group, the AUC was estimated to be 0.927. Additionally, it was found that values of sensitivity and specificity were 0.875 and 0.833, respectively. In the validation group, the AUC of the integrated model showed a significant improvement when contrasted to the clinical-radiological model (p = 0.042). Nevertheless, no significant distinction was observed in the AUC when comparing the integrated model with the radiomics model (p = 0.734). The DCA suggested that the integrated model demonstrates advantageous clinical utility.Conclusion: The integrated model, which combines the CT radiomics signature and the clinical-radiological signature, exhibited higher predictive efficacy than either the radiomics or clinical-radiological models alone. This suggests that during the prediction of the objective responsiveness of HCC patients to the first DEB-TACE treatment, the integrated model yields superior outcomes.https://www.frontiersin.org/articles/10.3389/fphar.2024.1315732/fullhepatocellular carcinomadrug-eluting beads transcatheter arterial chemoembolizationinitial responseobjective responsecomputed tomographyradiomics |
| spellingShingle | Xueying Zhang Zijun He Yucong Zhang Jian Kong Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model Frontiers in Pharmacology hepatocellular carcinoma drug-eluting beads transcatheter arterial chemoembolization initial response objective response computed tomography radiomics |
| title | Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model |
| title_full | Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model |
| title_fullStr | Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model |
| title_full_unstemmed | Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model |
| title_short | Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model |
| title_sort | prediction of initial objective response to drug eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using ct radiomics based machine learning model |
| topic | hepatocellular carcinoma drug-eluting beads transcatheter arterial chemoembolization initial response objective response computed tomography radiomics |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1315732/full |
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