Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma

Parvaze, P. Suhail; Bhattacharjee, Rupsa; Singh, Anup; Ahlawat, Sunita; Patir, Rana; Vaishya, Sandeep; Shah, Tushar A.; Gupta, Rakesh K.

doi:10.1016/j.ejrad.2022.110655

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…The radiomics features were analyzed with the SPSS statistical analysis tool and visualization plots were generated for selected significant features using Python 3.7 and libraries including Scikit learn 0.22 and Open‐CV 4.1.2. In the current study, 2D feature maps were computed, because they closely resemble the anatomy of the slice studied 22 . The representative radiomics feature maps were further used to overlay the FLAIR images for qualitative visualization using in‐house Python code.…”

Section: Methodsmentioning

confidence: 99%

“…In the current study, 2D feature maps were computed, because they closely resemble the anatomy of the slice studied. 22 The representative radiomics feature maps were further used to overlay the FLAIR images for qualitative visualization using in-house Python code. In the overlay mechanism, three colors were used to depict the feature values, with blue to represent low feature values, green to demonstrate intermediate values, and red to represent high feature values.…”

Section: Radiomics Analysismentioning

confidence: 99%

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Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid‐attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1‐dynamic contrast‐enhanced Perfusion analysis

et al. 2022

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The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1‐weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0‐T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1‐dynamic contrast‐enhanced MR imaging. Statistically significant (< 0.05, independent samples T‐test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1‐weighted cell line images but group comparison was carried out using one‐way analysis of variance. Further, a random forest (RF)‐based machine learning model was designed to classify the PVE of GB and BM. Texture‐based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.

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Section: Methodsmentioning

confidence: 99%

Section: Radiomics Analysismentioning

confidence: 99%

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid‐attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1‐dynamic contrast‐enhanced Perfusion analysis

et al. 2022

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“…Subregional analysis has shown that radiomic metrics are capable of identifying distinct subpopulations that are more aggressive and treatment-resistant by exploring imaging features across the whole tumor, whose first step is segmentation of the tumor into several subregions, e.g., necrosis, enhancing core and peritumoral edema, by neuroradiologists or using deep learning segmentation methods (Chen et al 2019 ; Li et al 2018a , b ; Rudie et al 2019 ; Suhail et al 2023 ). An alternative is use of clustering algorithms,—this method is also known as ‘ habitat imaging ’—which generates functionally coherent subregions of the tumor (Gatenby et al 2013 ; Juan-Albarracin et al 2018 ; Kim et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

Wei,

Lu,

Lai

et al. 2024

J Cancer Res Clin Oncol

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Purpose To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. Methods 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. Results The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ($${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$ F seq 1 , 2 , 3 , 4 ) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features $${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$ F seq 1 , 2 , 3 , 4 achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). Conclusion The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.

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“…Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults (Chougule et al., 2022 ; Gonçalves et al., 2020 ), with high recurrence and mortality rates despite standard therapies (Campos et al., 2016 ; Parvaze et al., 2023 ). Molecular classification of GBM has been proposed to identify subtypes with distinct clinical, genetic, and epigenetic features for risk stratification (Gritsch et al., 2022 ; Yang et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

Chen,

et al. 2023

Brain and Behavior

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Background and purposeThe presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients.MethodsA total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10‐fold cross validation was applied to the training dataset.ResultsA model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations.ConclusionsRadiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.

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Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma

Cited by 7 publications

References 32 publications

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid‐attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1‐dynamic contrast‐enhanced Perfusion analysis

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid‐attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1‐dynamic contrast‐enhanced Perfusion analysis

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

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