18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study
Abstract Background This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in 18F-FDG PET/CT images. Methods A total of 137 HCC patients from two institutions we...
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BMC
2024-11-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-024-13206-5 |
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| author | Chunxiao Sui Qian Su Kun Chen Rui Tan Ziyang Wang Zifan Liu Wengui Xu Xiaofeng Li |
| author_facet | Chunxiao Sui Qian Su Kun Chen Rui Tan Ziyang Wang Zifan Liu Wengui Xu Xiaofeng Li |
| author_sort | Chunxiao Sui |
| collection | DOAJ |
| description | Abstract Background This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in 18F-FDG PET/CT images. Methods A total of 137 HCC patients from two institutions were retrospectively included. First, intratumoral habitats were achieved by a two-step unsupervised clustering process based on k-means clustering. Second, a total of 4032 radiomic features were extracted based on each habitat, including 2016 PET-based and 2016 CT-based radiomic features. Then, after feature selection, the stacking ensemble learning approach which combined six machine learning classifiers as the first-level learners with Cox proportional hazards regression as the second-level learner, was employed to build multiple radiomic models. Finally, the optimal model was selected based on the calculation of the C-index, and a combined model integrating with a clinical model was also constructed to identify the potentially complementary effect. Results Three spatially distinct habitats were identified in the two cohorts. Among a total of 30 stacking ensemble learning models established based on different combinations of 5 types of segmented volumes of interest (VOIs) with 6 types of classifiers, the MLP-Cox-habitat-2 model was selected as the optimal radiomic model with a C-index of 0.702 in the external validation cohort. Furthermore, the combined model integrating the optimal radiomic model with the clinical model achieved an improved C-index of 0.747. Consistently, the combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.835, 0.828, and 0.800 in the 1-year, 2-year, and 3-year OS, respectively. Conclusion 18F-FDG PET/CT-based habitat radiomics outperformed traditional radiomics in OS prediction for HCC, with a further improved predictive power by integrating with the clinical model. The optimal combined habitat model was potentially promising in guiding individualized treatment for HCC. Trial registration This study was a retrospective study, so it was free from registration. |
| format | Article |
| id | doaj-art-311bb8d0abaa4df9a53a472b8424ac70 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-311bb8d0abaa4df9a53a472b8424ac702024-12-01T12:30:10ZengBMCBMC Cancer1471-24072024-11-0124111110.1186/s12885-024-13206-518F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center studyChunxiao Sui0Qian Su1Kun Chen2Rui Tan3Ziyang Wang4Zifan Liu5Wengui Xu6Xiaofeng Li7Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and HospitalDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and HospitalZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport HospitalDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and HospitalDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and HospitalDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and HospitalAbstract Background This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in 18F-FDG PET/CT images. Methods A total of 137 HCC patients from two institutions were retrospectively included. First, intratumoral habitats were achieved by a two-step unsupervised clustering process based on k-means clustering. Second, a total of 4032 radiomic features were extracted based on each habitat, including 2016 PET-based and 2016 CT-based radiomic features. Then, after feature selection, the stacking ensemble learning approach which combined six machine learning classifiers as the first-level learners with Cox proportional hazards regression as the second-level learner, was employed to build multiple radiomic models. Finally, the optimal model was selected based on the calculation of the C-index, and a combined model integrating with a clinical model was also constructed to identify the potentially complementary effect. Results Three spatially distinct habitats were identified in the two cohorts. Among a total of 30 stacking ensemble learning models established based on different combinations of 5 types of segmented volumes of interest (VOIs) with 6 types of classifiers, the MLP-Cox-habitat-2 model was selected as the optimal radiomic model with a C-index of 0.702 in the external validation cohort. Furthermore, the combined model integrating the optimal radiomic model with the clinical model achieved an improved C-index of 0.747. Consistently, the combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.835, 0.828, and 0.800 in the 1-year, 2-year, and 3-year OS, respectively. Conclusion 18F-FDG PET/CT-based habitat radiomics outperformed traditional radiomics in OS prediction for HCC, with a further improved predictive power by integrating with the clinical model. The optimal combined habitat model was potentially promising in guiding individualized treatment for HCC. Trial registration This study was a retrospective study, so it was free from registration.https://doi.org/10.1186/s12885-024-13206-518F-FDG PET/CTHabitat radiomicsStacking ensemble learningHCCPrognosis |
| spellingShingle | Chunxiao Sui Qian Su Kun Chen Rui Tan Ziyang Wang Zifan Liu Wengui Xu Xiaofeng Li 18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study BMC Cancer 18F-FDG PET/CT Habitat radiomics Stacking ensemble learning HCC Prognosis |
| title | 18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study |
| title_full | 18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study |
| title_fullStr | 18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study |
| title_full_unstemmed | 18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study |
| title_short | 18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study |
| title_sort | 18f fdg pet ct based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma a multi center study |
| topic | 18F-FDG PET/CT Habitat radiomics Stacking ensemble learning HCC Prognosis |
| url | https://doi.org/10.1186/s12885-024-13206-5 |
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