Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics

Choi, Yangsean; Ahn, Kook Jin; Nam, Yoonho; Jang, Jinhee; Shin, Na‐Young; Choi, Hyun Seok; Jung, Seungwon; Kim, Bum Soo

doi:10.1016/j.ejrad.2019.108642

Cited by 30 publications

(35 citation statements)

References 35 publications

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“…This is in line with the hypothesis that the T2-weighted signal corresponds with intratumor heterogeneity and infiltrative tumor growth [39] and this area is accountable for the majority of local recurrences [40]. Therefore, radiomics features from this area are expected to be of importance for survival prediction as was also shown in previous studies [41,42]. The radiomics signature for OS consists of five features, from which two features are the first order Mean (T2-weighted) and Median (T1-weighted) describing the mean and median intensity values after the LLH and HHH wavelet decomposition of the original MR images.…”

Section: Discussionsupporting

confidence: 89%

“…The combined model was also able to accurately split the two cohorts in a high-and low-risk group (p-value < 0.001) ( Figure 1B,C). Previous studies also observed that combining clinical features with imaging features improves the prognostic value of the model [42,[44][45][46][47]. The model developed in this study performed similar or better compared to previous findings, even after external validation in a heterogeneous patient cohort.…”

Section: Discussionsupporting

confidence: 79%

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Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

Verduin

Primakov

Compter

et al. 2021

Cancers

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Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 79%

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

Verduin

Primakov

Compter

et al. 2021

Cancers

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“…31 More recently, the radiomic features of the peritumoral T2 hyperintensity using texture analysis in patients with pretreatment GBM suggested incremental prognostic value of peritumoral radiomics as a MRI biomarker in pretreatment glioblastoma. 32 Although the lower ADC values in the tumour region of PCNSL compared to GBM have previously been reported, 8,9,33 there are only a few studies comparing the ADC values in the peritumoral region of PCNSL and GBM. Lu et al.…”

Section: Discussionmentioning

confidence: 97%

Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients

et al. 2020

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Purpose The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients. Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma. Results Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group ( P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1–99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9–92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group ( P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2–100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3–95.8%). Conclusions Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.

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“…Our study showed that a combination of age and histogram based first-order textures extracted from the whole tumor could predict survival with good diagnostic performance (accuracy of 70%). Choi et al 19 also showed that a combination of clinical and texture features had higher predictive performance compared to radiomics alone. Similarly, Ingrisch et al 24 found a significant association of whole tumor MRTA with survival in GBM.…”

Section: Discussionmentioning

confidence: 99%

“…However, all these studies utilized a selected combination of features based on shape, volume, first-, second-, and higherorder texture features, or deep features that were extracted from multiparametric or advanced imaging sequences (Appendix 1). 12,[15][16][17][18][19][20][21][22][23][24][25][26][27] Advanced sequences are not performed routinely at all centers, require additional expertise and expense, and are time-consuming. Additionally, the extraction of higher-order features requires multiple post-processing steps and is timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models

et al. 2021

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Objective Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis. Methods We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Results The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4. Conclusions First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

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Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics

Cited by 30 publications

References 35 publications

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients

Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models

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