Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas

Bai, Jie; Yang, Hongxi; Gao, Ankang; Wang, Yida; Zhao, Guohua; Wang, Chenglong; Wang, Haijie; Zhang, Xiaonan; Yang, Guang; Zhang, Yong; Cheng, Jingliang

doi:10.3389/fonc.2022.856359

Cited by 13 publications

(10 citation statements)

References 44 publications

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“…Radiomics is an emerging field, that has developed rapidly in recent years with the development of precision medicine [ 28 ]. Radiomics uses many automated data characterization algorithms to convert images of the ROI into quantitative high-throughput features, which radiologists cannot do with the naked eye [ 29 ]. By analyzing and calculating the quantitative features extracted from medical images to reflect information about tumor biology and microenvironment, it can elaborate on intra-tumor heterogeneity more effectively and accurately.…”

Section: Discussionmentioning

confidence: 99%

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

Feng

Yang

et al. 2022

Insights Imaging

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Objective To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma. Methods A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA). Results Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility. Conclusion The 18F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma.

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Section: Discussionmentioning

confidence: 99%

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

Feng

Yang

et al. 2022

Insights Imaging

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“…It has some strengths compared to previous research. Firstly, it specifically focuses on predicting epilepsy development in ALC patients with BM, which sets it apart from studies of Pan et al (2021) and Jie et al (2022) that focused on patients with brain tumors in general. This allows for a more specific and tailored prediction model for ALC patients with BM.…”

Section: Discussionmentioning

confidence: 99%

Influencing Factors and Nomogram for the Development of Epilepsy in Advanced Lung Cancer Patients With Brain Metastases

Yuan

Tao

et al. 2023

Biological Research For Nursing

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Background Epilepsy is a prevalent comorbidity in patients with brain metastases (BM) and could result in sudden and accidental damage, as well as increased disease burden due to its rapid onset. Foreseeing the potential for the development of epilepsy may permit timely and efficient measures. This study aimed to analyze the influencing factors of epilepsy in advanced lung cancer (ALC) patients with BM and construct a nomogram model to predict the likelihood of developing epilepsy. Methods Socio-demographic and clinical data of ALC patients with BM were retrospectively collected from the First Affiliated Hospital of Zhejiang University School of Medicine between September 2019 and June 2021. Univariate and multivariate logistic regression analyses were applied to determine the influencing factors for epilepsy in ALC patients with BM. Based on the results of the logistic regression analysis, a nomogram was built to represent the contribution of each influencing factor in predicting the probability of epilepsy development in ALC patients with BM. The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were utilized to evaluate the goodness of fit and prediction performance of the model. Results The incidence of epilepsy among 138 ALC patients with BM was 29.7%. On the multivariate analysis, having a higher number of supratentorial lesions (odds ratio [OR] = 1.727; p = 0.022), hemorrhagic foci (OR = 4.922; p = .021), and a high-grade of peritumoral edema (OR = 2.524; p < .001) were independent risk factors for developing epilepsy, while undergoing gamma knife radiosurgery (OR = .327; p = .019) was an independent protective factor. The p-value of the Hosmer-Lemeshow test was .535 and the area under the ROC curve (AUC) was .852 (95% CI: .807–.897), suggesting the model had a good fit and exhibited strong predictive accuracy. Conclusion The nomogram was constructed that can predict the probability of epilepsy development for ALC patients with BM, which is helpful for healthcare professionals to identify high-risk groups early and allows for individualized interventions.

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“…For the classifier, we compared the performance of the linear support vector machine (SVM) and logistic regression (LR). To find the best model for each subgroup, we tested different combinations of feature selectors and classifiers and selected the combination with the best cross-validation area under the receiver operating characteristic (ROC) curve (AUC) ( 21 , 22 ). We used 5-fold cross-validation to determine the hyper-parameter, which was set according to the model performance with the validation dataset.…”

Section: Methodsmentioning

confidence: 99%

“…These six features were most significantly related to RTLI. Incorporating the above-selected radiomics signatures and three independent clinical factors (gender, N stage, and T stage) by logistic regression analysis, a radiomics–clinics combined model was built using the logistic regression method ( 21 ). The combined model was visualized as a radiomics nomogram.…”

Section: Methodsmentioning

confidence: 99%

MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma

et al. 2023

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ObjectiveThis study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThis retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training (n = 91) and testing (n = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics–clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model.ResultsSix texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306–0.9939] and 0.904 (95% CI, 0.8431–0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841–1.0000) and 0.891 (95% CI, 0.7903–0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect.ConclusionThe radiomics–clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

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Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas

Cited by 13 publications

References 44 publications

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

Influencing Factors and Nomogram for the Development of Epilepsy in Advanced Lung Cancer Patients With Brain Metastases

MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma

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