Differentiation of grade II/III and grade IV glioma by combining “T1 contrast-enhanced brain perfusion imaging” and susceptibility-weighted quantitative imaging

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Background Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed. Purpose To develop an artificial intelligence‐based methodology for glioma grading using T1 perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients. Study Type Retrospective. Population The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients. Field Strength/Sequence Conventional MRI images (2D T1‐W, dual PD‐T2‐W, and 3D FLAIR) and 3D T1 perfusion MRI data obtained at 3 T. Assessment Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T1 perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T1 perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest‐based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading. Statistical Tests Mean ± SD, analysis of variance (ANOVA) followed by the Tukey–Kramer test, ROC analysis. Results Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90th percentile value of T1 perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier. Data Conclusion It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. Level of Evidence: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295–1306.

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“…CBV is considered to be a biomarker of tumor angiogenesis . However, it has been reported that CBV values overlap between intermediate grades (Grade II vs. III and Grade III vs. IV) …”

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Glioma grading using a machine‐learning framework based on optimized features obtained from T₁ perfusion MRI and volumes of tumor components

Sengupta

Ramaniharan

Gupta

et al. 2019

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Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma

Bhattacharjee

Gupta

Patir

et al. 2019

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Background: Susceptibility weighted imaging (SWI) provides vascular information and plays an important role in improving the diagnostic accuracy of preoperative glioma grading. Intratumoral susceptibility signal intensities (ITSS) obtained from SWI has been used in glioma grading. However, the current method for estimation of ITSS is semiquantitative, manual count-dependent, and includes hemorrhage as well as vasculature. Purpose: To develop a quantitative approach that calculates the vasculature volume within tumors by filtering out the hemorrhage from ITSS using R 2 * values and connected component analysis-based segmentation algorithm; to evaluate the accuracy of the proposed ITSS vasculature volume (IVV) for differentiating various grades of glioma; and compare it with reported semiquantitative ITSS approach. Study Type: Retrospective. Subjects: Histopathologically confirmed 41 grade IV, 19 grade III, and 15 grade II glioma patients.Field Strength/Sequence: SWI (four echoes: 5.6, 11.8, 18, 24.2 msec) along with conventional MRI sequences (T 2 -weighted, T 1 -weighted, 3D-fluid-attenuated inversion recovery [FLAIR], and diffusion-weighted imaging [DWI]) at 3.0T. Assessment: R 2 * relaxation maps were calculated from multiecho SWI. The R 2 * cutoff value for hemorrhage ITSS was determined. A segmentation algorithm was designed, based on this R 2 * hemorrhage combined with connected component shape analysis, to quantify the IVV from all slices containing tumor by filtering out hemorrhages. Semiquantitative ITSS scoring as well as total ITSS volume (TIV) including hemorrhages were also calculated. Statistical Tests: One-way analysis of variance (ANOVA) and Tukey-Kramer post-hoc tests were performed to see the difference among the three grades of the tumor (II, III, and IV) in terms of semiquantitative ITSS scoring, TIV, and IVV. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the three methods individually in discriminating between grades of glioma. Results: One-way ANOVA showed that only the proposed IVV significantly differentiated different grades of gliomas having visible ITSS. ROC analysis showed that IVV provided the highest AUC for the discrimination of grade II vs. III (0.93), grade III vs. IV (0.98), and grade II vs. IV glioma (0.94). IVV also provided the highest sensitivity and specificity for differentiating grade II vs. III (87.44, 98.41), grade III vs. IV (97.15, 94.12), and grade II vs. IV (98.72, 92.31). Data Conclusion: The proposed quantitative method segregates hemorrhage from tumor vasculature. It scores above the existing semiquantitative method in terms of ITSS estimation and grading accuracy.

AdvancedMRTechniques for Preoperative Glioma Characterization: Part 2

Hangel

Schmitz-Abecassis

Sollmann

et al. 2023