Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors
Abstract Objective To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arteri...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s12876-024-03555-7 |
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| author | Linan Yin Ruibao Liu Wei Li Shijie Li Xunbo Hou |
| author_facet | Linan Yin Ruibao Liu Wei Li Shijie Li Xunbo Hou |
| author_sort | Linan Yin |
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| description | Abstract Objective To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arterial infusion chemotherapy (HAIC) combined with PD-1 inhibitors and tyrosine kinase inhibitors (TKIs). Methods This retrospective study included 172 patients with uHCC who underwent combination therapy of TACE-HAIC with TKIs and PD-1 inhibitors. Among them, 122 were from the Interventional Department of the Harbin Medical University Cancer Hospital, with 92 randomly assigned to the training cohort and 30 cases randomly assigned to the testing cohort. The remaining 50 cases were from the Interventional Department of the Affiliated Fourth Hospital of Harbin Medical University and were used for external validation. All patients underwent liver enhanced CT examination before treatment. Residual convolutional neural network (ResNet) technology was used to extract image features. A predictive model for treatment response of combination therapy and PFS was established based on image features and clinical features. Model effectiveness was evaluated using metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), concordance index (C-index), accuracy, precision, and F1-score. Results All patients had a median follow-up of 25.2 months (95% CI 24.4–26.0), with a median PFS of 14.0 months (95% CI 8.5–19.4) and a median overall survival (OS) of 26.2 months (95% CI 15.9–36.4) achieved. Objective response rate (ORR) and disease control rate (DCR) was 41.0% and 55.7%, respectively. In the treatment response prediction model, the AUC for the training cohort reached 0.96, with an accuracy of 89.5%, precision of 85.6%, and F1-score of 0.896; the AUC for the testing cohort was 0.87, with an accuracy of 80.4%, precision of 74.5%, and F1-score of 0.802. The AUC of the external validation cohort was 0.85, with accuracy of 79.1%, precision of 73.6%, and f1-score of 0.784. In the PFS prediction model, the predicted AUC for 12 months, 18 months, and 24 months-PFS in the training cohort were 0.874, 0.809, 0.801, respectively. The AUC of testing cohort were 0.762, 0.804, 0.792. The AUC of external validation cohort were 0.764, 0.796, 0.773. The C-index of the combination model, radiomics model, and clinical model were 0.75, 0.591, and 0.655, respectively. The calibration curve demonstrated that the combination model was significantly superior to both the radiomics and clinical models. Conclusions The study provides a CT-based radiomics model that can predict PFS for patients with uHCC treated with TACE-HAIC combined with PD-1 and TKIs. |
| format | Article |
| id | doaj-art-84d9155910844866b1c391ddde48592a |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| series | BMC Gastroenterology |
| spelling | doaj-art-84d9155910844866b1c391ddde48592a2025-01-26T12:36:19ZengBMCBMC Gastroenterology1471-230X2025-01-0125111310.1186/s12876-024-03555-7Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitorsLinan Yin0Ruibao Liu1Wei Li2Shijie Li3Xunbo Hou4Department of Interventional Radiology, Harbin Medical University Cancer HospitalDepartment of Interventional Radiology, Harbin Medical University Cancer HospitalDepartment of Interventional Radiology, Affiliated Fourth Hospital of Harbin Medical UniversityDepartment of Interventional Radiology, Harbin Medical University Cancer HospitalDepartment of Interventional Radiology, Harbin Medical University Cancer HospitalAbstract Objective To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arterial infusion chemotherapy (HAIC) combined with PD-1 inhibitors and tyrosine kinase inhibitors (TKIs). Methods This retrospective study included 172 patients with uHCC who underwent combination therapy of TACE-HAIC with TKIs and PD-1 inhibitors. Among them, 122 were from the Interventional Department of the Harbin Medical University Cancer Hospital, with 92 randomly assigned to the training cohort and 30 cases randomly assigned to the testing cohort. The remaining 50 cases were from the Interventional Department of the Affiliated Fourth Hospital of Harbin Medical University and were used for external validation. All patients underwent liver enhanced CT examination before treatment. Residual convolutional neural network (ResNet) technology was used to extract image features. A predictive model for treatment response of combination therapy and PFS was established based on image features and clinical features. Model effectiveness was evaluated using metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), concordance index (C-index), accuracy, precision, and F1-score. Results All patients had a median follow-up of 25.2 months (95% CI 24.4–26.0), with a median PFS of 14.0 months (95% CI 8.5–19.4) and a median overall survival (OS) of 26.2 months (95% CI 15.9–36.4) achieved. Objective response rate (ORR) and disease control rate (DCR) was 41.0% and 55.7%, respectively. In the treatment response prediction model, the AUC for the training cohort reached 0.96, with an accuracy of 89.5%, precision of 85.6%, and F1-score of 0.896; the AUC for the testing cohort was 0.87, with an accuracy of 80.4%, precision of 74.5%, and F1-score of 0.802. The AUC of the external validation cohort was 0.85, with accuracy of 79.1%, precision of 73.6%, and f1-score of 0.784. In the PFS prediction model, the predicted AUC for 12 months, 18 months, and 24 months-PFS in the training cohort were 0.874, 0.809, 0.801, respectively. The AUC of testing cohort were 0.762, 0.804, 0.792. The AUC of external validation cohort were 0.764, 0.796, 0.773. The C-index of the combination model, radiomics model, and clinical model were 0.75, 0.591, and 0.655, respectively. The calibration curve demonstrated that the combination model was significantly superior to both the radiomics and clinical models. Conclusions The study provides a CT-based radiomics model that can predict PFS for patients with uHCC treated with TACE-HAIC combined with PD-1 and TKIs.https://doi.org/10.1186/s12876-024-03555-7Deep learning modelRadiomicsPrognosisUnresectable liver cancer |
| spellingShingle | Linan Yin Ruibao Liu Wei Li Shijie Li Xunbo Hou Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors BMC Gastroenterology Deep learning model Radiomics Prognosis Unresectable liver cancer |
| title | Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors |
| title_full | Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors |
| title_fullStr | Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors |
| title_full_unstemmed | Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors |
| title_short | Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors |
| title_sort | deep learning based ct radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with tace haic combined with pd 1 inhibitors and tyrosine kinase inhibitors |
| topic | Deep learning model Radiomics Prognosis Unresectable liver cancer |
| url | https://doi.org/10.1186/s12876-024-03555-7 |
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