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Wu H, Liao K, Tan Z, Zeng C, Wu B, Zhou Z, Zhou H, Tang Y, Gong J, Ye W, Ling X, Guo Q, Xu H. A PET-based radiomics nomogram for individualized predictions of seizure outcomes after temporal lobe epilepsy surgery. Seizure 2024; 119:17-27. [PMID: 38768522 DOI: 10.1016/j.seizure.2024.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/26/2024] [Accepted: 04/21/2024] [Indexed: 05/22/2024] Open
Abstract
PURPOSE To establish and validate a novel nomogram based on clinical characteristics and [18F]FDG PET radiomics for the prediction of postsurgical seizure freedom in patients with temporal lobe epilepsy (TLE). PATIENTS AND METHODS 234 patients with drug-refractory TLE patients were included with a median follow-up time of 24 months after surgery. The correlation coefficient redundancy analysis and LASSO Cox regression were used to characterize risk factors. The Cox model was conducted to develop a Clinic-PET nomogram to predict the relapse status in the training set (n = 171). The nomogram's performance was estimated through discrimination, calibration, and clinical utility. The prognostic prediction model was validated in the test set (n = 63). RESULTS Eight radiomics features were selected to assess the radiomics score (radscore) of the operation side (Lat_radscore) and the asymmetric index (AI) of the radiomics score (AI_radscore). AI_radscor, Lat_radscor, secondarily generalized seizures (SGS), and duration between seizure onset and surgery (Durmon) were significant predictors of seizure-free outcomes. The final model had a C-index of 0.68 (95 %CI: 0.59-0.77) for complete freedom from seizures and time-dependent AUROC was 0.65 at 12 months, 0.65 at 36 months, and 0.59 at 60 months in the test set. A web application derived from the primary predictive model was displayed for economic and efficient use. CONCLUSIONS A PET-based radiomics nomogram is clinically promising for predicting seizure outcomes after temporal lobe epilepsy surgery.
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Affiliation(s)
- Huanhua Wu
- The Affiliated Shunde Hospital of Jinan University, Foshan, Guangdong Province 528305, PR China
| | - Kai Liao
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China
| | - Zhiqiang Tan
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China
| | - Chunyuan Zeng
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China
| | - Biao Wu
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China
| | - Ziqing Zhou
- Department of Nuclear Medicine, Nanhai District People's Hospital of Foshan, Foshan, Guangdong Province, 528225, PR China
| | - Hailing Zhou
- Department of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong Province, 524045, PR China
| | - Yongjin Tang
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China
| | - Jian Gong
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China
| | - Weijian Ye
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China
| | - Xueying Ling
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China.
| | - Qiang Guo
- Epilepsy Center, Guangdong 999 Brain Hospital, Affiliated Brain Hospital of Jinan University, Guangzhou, Guangdong Province 510510, PR China.
| | - Hao Xu
- Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China.
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Ghazou A, Yassin A, Aljabali AS, Al-Zamer YS, Alawajneh M, Al-Akhras A, AlBarakat MM, Tashtoush S, Shammout O, Al-Horani SS, Jarrah EE, Ababneh O, Jaradat A. Predictors of early and late postoperative seizures in meningioma patients: a systematic review and meta-analysis. Neurosurg Rev 2024; 47:242. [PMID: 38806755 DOI: 10.1007/s10143-024-02487-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/27/2024] [Accepted: 05/25/2024] [Indexed: 05/30/2024]
Abstract
Meningioma is the most common type of primary brain tumor which presents with a variety of neurological manifestations. Surgical resection tends to be the preferred treatment. The occurrence of seizures after resection is common, which occur either early, within seven days of operation, or late. Our meta-analysis investigated the possible predictors of early and late postoperative seizures. We assessed the relevant observational studies on predictors of postoperative seizures published in PubMed, Scopus, and Web of Science from January 2000 to September 2022, and those that met inclusion criteria were included. We calculated the association between potential predicting factors and postoperative seizures, odds ratios (ORs) with 95% confidence intervals (CIs) applying either random or fixed-effect models. The early and late postoperative seizures were evaluated individually. Thirteen observational studies involving 4176 patients were included. Seizures occurred in 250 (6%) and 584 (14%) patients, respectively, in the early and late postoperative phases. Shared predictors for early and late seizures included tumors involving the motor cortex (OR = 2.7; 95% CI: 1.67-4.38, OR = 2.46; 95% CI: 1.68-3.61), postoperative neurological deficit (OR = 4.68; 95% CI: 2.67-8.22, OR = 2.01; 95% CI: 1.39-2.92), and preoperative seizures (OR = 2.52; 95% CI: 1.82-3.49, OR = 4.35; 95% CI: 3.29-5.75). Peritumoral edema (OR = 1.99; 95% CI: 1.49-2.64) was a significant factor only among late postoperative seizure patients while surgical complications (OR = 3.77; 95% CI: 2.39-5.93) was a significant factor solely for early postoperative seizures. Meningioma patients commonly experience early and late postoperative seizures. Identifying predictors of postoperative seizures is essential to diagnose and manage them effectively.
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Affiliation(s)
- Alina Ghazou
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
| | - Ahmed Yassin
- Department of Neurology, Jordan University of Science and Technology, Irbid, Jordan
| | - Ahmed S Aljabali
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Yazan S Al-Zamer
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Mohmmad Alawajneh
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Ashaar Al-Akhras
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Majd M AlBarakat
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Shaden Tashtoush
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Omar Shammout
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Sami S Al-Horani
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Eman E Jarrah
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Obada Ababneh
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Amer Jaradat
- Department of Neurosurgery, Jordan University of Science and Technology, Irbid, Jordan
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Han T, Liu X, Long C, Xu Z, Geng Y, Zhang B, Deng L, Jing M, Zhou J. Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging. Magn Reson Imaging 2023; 104:16-22. [PMID: 37734573 DOI: 10.1016/j.mri.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 08/10/2023] [Accepted: 09/17/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE To explore the clinical value of a clinical radiomics model nomogram based on magnetic resonance imaging (MRI) for preoperative meningioma grading. MATERIALS AND METHODS We collected retrospectively 544 patients with pathological diagnosis of meningiomas were categorized into training (n = 380) and validation (n = 164) groups at the ratio of 7∶ 3. There were 3,376 radiomics features extracted from T2WI and T1C by shukun technology platform after manual segmentation using an independent blind method by two radiologists. The Selectpercentile and Lasso are used to filter the most strongly correlated features. Random forest (RF) radiomics model and clinical radiomics model nomogram were constructed respectively. The calibration, discrimination, and clinical validity were evaluated by using the calibration curve and decision analysis curve (DCA). RESULTS The RF radiomics model based on T1C and T2WI was the most effective to predict meningioma grade before surgery among the six different classifiers. The predictive ability of clinical radiomics model was slightly higher than that of RF model alone. The AUC, SEN, SPE, and ACC of the training set were 0.949, 0.976, 0.785, and 0.826, and the AUC, SEN, SPE, and ACC of the validation set were 0.838, 0.829, 0.783, and 0.793, respectively. The calibration curve and Hosmer-Lemeshow test showed the predictive probability of the fusion model was similar to the actual differentiated LGM and HGM. The analysis of the decision curve showed that the clinical radiomics model could obtain the best clinical net profit. CONCLUSIONS The clinical radiomics model nomogram based on T1C and T2WI has high accuracy and sensitivity for predicting meningioma grade.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Changyou Long
- Image Center of Affiliated Hospital of Qinghai University, Xining, China
| | - Zhendong Xu
- Shukun (Beijing) Technology Co., Ltd., Jinhui Building, Qiyang Road, 100102 Beijing, China
| | - Yayuan Geng
- Shukun (Beijing) Technology Co., Ltd., Jinhui Building, Qiyang Road, 100102 Beijing, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China.
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Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
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Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
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Wang J, Luo X, Chen C, Deng J, Long H, Yang K, Qi S. Preoperative MRI for postoperative seizure prediction: a radiomics study of dysembryoplastic neuroepithelial tumor and a systematic review. Neurosurg Focus 2022; 53:E7. [DOI: 10.3171/2022.7.focus2254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/25/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE
In this systematic review the authors aimed to evaluate the effectiveness and superiority of radiomics in detecting tiny epilepsy lesions and to conduct original research in the use of radiomics for preliminary prediction of postoperative seizures in patients with dysembryoplastic neuroepithelial tumor (DNET).
METHODS
The PubMed and Web of Science databases were searched from the earliest record, January 1, 2018, to December 29, 2021, for reports of the detection of epilepsy using radiomics, and the resulting articles were carefully checked according to the PRISMA 2020 guidelines. The authors then conducted original research by evaluating MR images in 18 patients, who were then separated into two groups, the epilepsy recurrence group (ERG) and the epilepsy nonrecurrence group. The tumor region and the edema region were segmented manually by 3D Slicer. The radiomics data were extracted from MR images by using “Slicer Radiomics” running on Mac OS X. Tumor regions were observed with T1-weighted imaging, and edema with FLAIR imaging. Radiomics features with significant differences were selected through comparison according to epilepsy relapses performed with the Mann-Whitney U-test. The edema and tumor regions were also compared within groups to identify their distinctive features. Radiomics features were tested to verify their ability to predict recurrence epilepsy by receiver operating characteristic curve.
RESULTS
This systematic review located 9 original articles related to epilepsy and radiomics published from 2018 to 2021. The reported studies demonstrated that radiomics is useful for detecting tiny epilepsy lesions. Among the radiomics features used, the predictive ability of the area under the curve was more than 0.8. The heterogeneity of the peritumoral edema region was found to be higher in the ERG.
CONCLUSIONS
Satellite lesions in the peritumoral edema region of DNET patients may cause epilepsy recurrence, and radiomics is an emerging method to detect and evaluate these epilepsy-associated lesions.
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Affiliation(s)
- Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University
- The First Clinical Medicine College, Southern Medical University; and
- Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Xinyi Luo
- The First Clinical Medicine College, Southern Medical University; and
- Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Chenghan Chen
- The First Clinical Medicine College, Southern Medical University; and
- Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Jiahong Deng
- The First Clinical Medicine College, Southern Medical University; and
- Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Hao Long
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University
- The First Clinical Medicine College, Southern Medical University; and
| | - Kaijun Yang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University
- The First Clinical Medicine College, Southern Medical University; and
| | - Songtao Qi
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University
- The First Clinical Medicine College, Southern Medical University; and
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Cai Q, Wu Y, Wang S, Huang T, Tian Q, Wang J, Qin H, Feng D. Preoperative antiepileptic drug prophylaxis for early postoperative seizures in supratentorial meningioma: a single-center experience. J Neurooncol 2022; 158:59-67. [DOI: 10.1007/s11060-022-04009-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/06/2022] [Indexed: 11/30/2022]
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