Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.

Publication/Presentation Date

1-30-2024

Abstract

BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM.

METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets.

RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (

CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and

Volume

16

Issue

3

ISSN

2072-6694

Disciplines

Medicine and Health Sciences

PubMedID

38339340

Department(s)

Department of Pathology and Laboratory Medicine

Document Type

Article

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