Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study
Abstract Background More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determi...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12916-023-03121-0 |
| _version_ | 1827399476541652992 |
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| author | Zhongke Wang Yang Li Zeng He Shujing Li Kaixuan Huang Xianjun Shi Xiaoqin Sun Ruotong Ruan Chun Cui Ruodan Wang Li Wang Shengqing Lv Chunqing Zhang Zhonghong Liu Hui Yang Xiaolin Yang Shiyong Liu |
| author_facet | Zhongke Wang Yang Li Zeng He Shujing Li Kaixuan Huang Xianjun Shi Xiaoqin Sun Ruotong Ruan Chun Cui Ruodan Wang Li Wang Shengqing Lv Chunqing Zhang Zhonghong Liu Hui Yang Xiaolin Yang Shiyong Liu |
| author_sort | Zhongke Wang |
| collection | DOAJ |
| description | Abstract Background More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images. Methods Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis. Results PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70–0.83) than the LDA (AUC = 0.7506; 95% CI 0.68–0.82) and ANN models (AUC = 0.7425; 95% CI 0.67–0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71–0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71–0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74–0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75–0.87). Conclusions The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients. |
| first_indexed | 2024-03-08T19:46:32Z |
| format | Article |
| id | doaj.art-9798c867a77042008e7d5ddbd4da05f3 |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-03-08T19:46:32Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-9798c867a77042008e7d5ddbd4da05f32023-12-24T12:21:23ZengBMCBMC Medicine1741-70152023-12-0121111010.1186/s12916-023-03121-0Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre studyZhongke Wang0Yang Li1Zeng He2Shujing Li3Kaixuan Huang4Xianjun Shi5Xiaoqin Sun6Ruotong Ruan7Chun Cui8Ruodan Wang9Li Wang10Shengqing Lv11Chunqing Zhang12Zhonghong Liu13Hui Yang14Xiaolin Yang15Shiyong Liu16Department of Neurosurgery, Armed Police Hospital of ChongqingDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and EpidemiologyDepartment of Radiology, Xinqiao Hospital, Army Medical UniversityDepartment of Neurology, Xinqiao Hospital, Army Medical UniversityDepartment of Neurology, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Armed Police Hospital of ChongqingDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityDepartment of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical UniversityAbstract Background More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images. Methods Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis. Results PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70–0.83) than the LDA (AUC = 0.7506; 95% CI 0.68–0.82) and ANN models (AUC = 0.7425; 95% CI 0.67–0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71–0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71–0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74–0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75–0.87). Conclusions The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients.https://doi.org/10.1186/s12916-023-03121-0Tuberous sclerosis complexEpileptogenic tubersEpilepsy18F-FDG PETMachine learning |
| spellingShingle | Zhongke Wang Yang Li Zeng He Shujing Li Kaixuan Huang Xianjun Shi Xiaoqin Sun Ruotong Ruan Chun Cui Ruodan Wang Li Wang Shengqing Lv Chunqing Zhang Zhonghong Liu Hui Yang Xiaolin Yang Shiyong Liu Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study BMC Medicine Tuberous sclerosis complex Epileptogenic tubers Epilepsy 18F-FDG PET Machine learning |
| title | Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study |
| title_full | Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study |
| title_fullStr | Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study |
| title_full_unstemmed | Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study |
| title_short | Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study |
| title_sort | predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18f fdg pet an 8 year single centre study |
| topic | Tuberous sclerosis complex Epileptogenic tubers Epilepsy 18F-FDG PET Machine learning |
| url | https://doi.org/10.1186/s12916-023-03121-0 |
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