Abstract
The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma. A total of 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [F]FET PET prior to surgical intervention were included. Patients with a survival time ≤ 12 months were classified as short-term survivors. First order, shape, and texture radiomic features were extracted from pre-treatment static (tumor-to-background ratio; TBR) and dynamic (time-to-peak; TTP) images, respectively, and randomly divided into a training (n = 99) and a testing cohort (n = 42). After feature normalization, recursive feature elimination was applied for feature selection using 5-fold cross-validation on the training cohort, and a machine learning model was constructed to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the predictive performance for identifying short-term survivors in both the training and testing cohort. A combined clinical-radiomic model comprising six clinical parameters and six selected dynamic radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60-0.88) in the independent testing cohort. This study successfully built and evaluated prediction models using [F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [F]FET PET-based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, our data shows that the integration of dynamic [F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers.
Overview
- The study aims to build and evaluate a prediction model for short-term survival in patients with newly diagnosed IDH-wildtype glioblastoma using [F]FET PET-based radiomic features and clinical parameters. The study includes 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [F]FET PET prior to surgical intervention. The study uses first order, shape, and texture radiomic features extracted from pre-treatment static (TBR) and dynamic (TTP) images, respectively, and recursive feature elimination for feature selection. The study constructs a machine learning model to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The study calculates the area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values to assess the predictive performance for identifying short-term survivors in both the training and testing cohort. The study successfully builds and evaluates prediction models using [F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [F]FET PET-based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, the study shows that the integration of dynamic [F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers.
Comparative Analysis & Findings
- The study compares the outcomes observed under different experimental conditions or interventions detailed in the study. The study identifies significant differences in the results between the radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The study finds that the combined clinical-radiomic model comprising six clinical parameters and six selected dynamic [F]FET PET-based radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60-0.88) in the independent testing cohort. The study shows that the integration of dynamic [F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers.
Implications and Future Directions
- The study's findings suggest that the integration of dynamic [F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers. The study identifies limitations, such as the small sample size and the need for further validation in larger cohorts. The study suggests future research directions, such as the exploration of other radiomic features and the integration of additional clinical parameters, to improve the predictive performance of the models. The study also suggests the need for further validation in larger cohorts to confirm the findings and to explore the clinical utility of the models.