Dataset Information

On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI.

ABSTRACT: Primary glioblastoma (GBM), IDH-wildtype, especially with multifocal appearance/growth (mGBM), is associated with very poor prognosis. Several clinical parameters have been identified to provide prognostic value in both unifocal GBM (uGBM) and mGBM, but information about the influence of radiological parameters on survival for mGBM cohorts is scarce. This study evaluated the prognostic value of several volumetric parameters derived from magnetic resonance imaging (MRI). Data from the Department of Neurosurgery, Leipzig University Hospital, were retrospectively analyzed. Patients treated between 2014 and 2019, aged older than 18 years and with adequate peri-operative MRI were included. Volumetric assessment was performed manually. One hundred and eighty-three patients were included. Survival of patients with mGBM was significantly shorter (p < 0.0001). Univariate analysis revealed extent of resection, adjuvant therapy regimen, residual tumor volume, tumor necrosis volume and ratio of tumor necrosis to initial volume as statistically significant for overall survival. In multivariate Cox regression, however, only EOR (for uGBM and the entire cohort) and adjuvant therapy were independently significant for survival. Decreased ratio of tumor necrosis to initial tumor volume and extent of resection were associated with prolonged survival in mGBM but failed to achieve statistical significance in multivariate analysis.

SUBMITTER: Kasper J

PROVIDER: S-EPMC8167648 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI.

Kasper Johannes J Hilbert Nicole N Wende Tim T Fehrenbach Michael Karl MK Wilhelmy Florian F Jähne Katja K Frydrychowicz Clara C Hamerla Gordian G Meixensberger Jürgen J Arlt Felix F

Current oncology (Toronto, Ont.) 20210407 2

Primary glioblastoma (GBM), IDH-wildtype, especially with multifocal appearance/growth (mGBM), is associated with very poor prognosis. Several clinical parameters have been identified to provide prognostic value in both unifocal GBM (uGBM) and mGBM, but information about the influence of radiological parameters on survival for mGBM cohorts is scarce. This study evaluated the prognostic value of several volumetric parameters derived from magnetic resonance imaging (MRI). Data from the Department ...[more]

PMID: 33917207

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Project description:ObjectiveTo develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma.Materials and methodsOne hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (Ktrans), fractional volume of vascular plasma space (Vp), and fractional volume of extravascular extracellular space (Ve) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the "radiomics risk score" groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates.Results16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively).ConclusionWe developed and validated the "radiomics risk score" from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

Project description:BackgroundPseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.MethodsThe model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23).ResultsTwelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness.ConclusionsIncorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

Dataset Information

On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI.

Publications

On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI.

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