Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models.

OBJECTIVES: To evaluate the additional prognostic value of multiparametric MR-based radiomics in patients with glioblastoma when combined with conventional clinical and genetic prognostic factors. METHODS: In this single-center study, patients diagnosed with glioblastoma between October 2007 and December 2019 were retrospectively screened and grouped into training and test sets with a 7:3 distribution. Segmentations of glioblastoma using multiparametric MRI were performed automatically via a convolutional-neural network. Prognostic factors in the clinical model included age, sex, type of surgery/post-operative treatment, and tumor location; those in the genetic model included statuses of isocitrate dehydrogenase-1 mutation and O-6-methylguanine-DNA-methyltransferase promoter methylation. Univariate and multivariate Cox proportional hazards analyses were performed for overall survival (OS) and progression-free survival (PFS). Integrated time-dependent area under the curve (iAUC) for survival was calculated and compared between prognostic models via the bootstrapping method (performances were validated with prediction error curves). RESULTS: Overall, 120 patients were included (training set, 85; test set, 35). The mean OS and PFS were 25.5 and 18.6 months, respectively. The prognostic performances of multivariate models improved when radiomics was added to the clinical model (iAUC: OS, 0.62 to 0.73; PFS, 0.58 to 0.66), genetic model (iAUC: OS, 0.59 to 0.67; PFS, 0.59 to 0.65), and combined model (iAUC: OS, 0.65 to 0.73; PFS, 0.62 to 0.67). In the test set, the combined model (clinical, genetic, and radiomics) demonstrated robust validation for risk prediction of OS and PFS. CONCLUSIONS: Radiomics increased the prognostic value when combined with conventional clinical and genetic prognostic models for OS and PFS in glioblastoma patients. KEY POINTS: • CNN-based automatic segmentation of glioblastoma on multiparametric MRI was useful in extracting radiomic features. • Patients with glioblastoma with high-risk radiomics scores had poor overall survival (hazards ratio 8.33, p < 0.001) and progression-free survival (hazards ratio 3.76, p < 0.001). • MR-based radiomics improved the survival prediction when combined with clinical and genetic factors (overall and progression-free survival iAUC from 0.65 to 0.73 and 0.62 to 0.67, respectively; both p < 0.001).

MRI and Quantitative Magnetic Susceptibility Maps of the Brain after Serial Administration of Gadobutrol: A Longitudinal Follow-up Study.

Background The relationship between administration of macrocyclic gadolinium-based contrast agents and T1-weighted signal intensity (SI) change of the globus pallidus (GP) and dentate nucleus (DN) is, to the knowledge of the authors, not known. Purpose To determine if quantitative susceptibility mapping (QSM) can detect changes in magnetic susceptibility of the GP and DN after serial administration of macrocyclic gadobutrol in patients with primary brain tumors. Materials and Methods Patients diagnosed with primary brain tumors from August 2014 to February 2019 were eligible for this single-center retrospective study. Among 501 consecutive adult patients who were given at least one administration of gadobutrol, those who were previously administered an unknown or linear gadolinium-based contrast agent were excluded. Brain MRI scans with three-dimensional gradient-recalled-echo image phase data for QSM processing were reviewed. Regions of interest were drawn on the GP and DN on the basis of semiautomatic thresholding. Univariable generalized estimation equations were used to determine the associations between MRI measures (SI on T1-weighted images and magnetic susceptibility on QSM) and number of gadobutrol doses. Potential confounding factors were adjusted for in multivariable generalized estimating equation. Results Ninety patients (mean age, 51 years ± 17 [standard deviation]; 51 men) with 199 MRI scans were analyzed. In models adjusted for repeated observations between injections, the number of injections of gadobutrol was associated with the magnetic susceptibility of the GP (1.4 × 10-3 ppm/number of gadobutrol injections; P = .01) and DN (8.1 × 10-4 ppm/number of gadobutrol injections; P = .03). After adjustment for confounders, the number of gadobutrol injections remained an independent predictor of increased magnetic susceptibility in the GP (1.3 × 10-3 ppm/number of gadobutrol injections; 95% confidence interval: 0.39 × 10-3, -2.4 × 10-3; P = .006). There were no associations between number of gadobutrol injections and SI or magnetic susceptibility in the DN. Conclusion The magnetic susceptibility of the globus pallidus increased after serial administration of gadobutrol. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Wang and Prince in this issue.

Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy.

PURPOSE: To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs). METHODS: Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman's correlation coefficients (ρ) were computed. RESULTS: HGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67-0.91) and 0.74 (95% CI, 0.59-0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48-0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52-0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57-0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01). CONCLUSIONS: The 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.

IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.

PURPOSE: This study aimed to determine whether MR-based radiomics of glioblastoma can predict the isocitrate dehydrogenase-1 (IDH1) mutation status and compare predictive performances between manual and fully automatic deep-learning segmentations. METHOD: Forty-five glioblastoma patients with pretreatment T2-weighted MRIs were retrospectively evaluated. Manual segmentations of glioblastoma and peri-tumoral edema were trained via a deep neural network (V-Net). An independent external cohort of 137 glioblastoma patients from the Cancer Imaging Archive was also included (test set 1, n = 46; test set 2, n = 91). Test set 1-without known IDH1 status-was used to calculate dice similarity coefficients (DSC) between the two segmentation methods (manual & V-Net). From test set 2, all-relevant radiomic features were selected via a random forest-based wrapper algorithm for IDH1 prediction. Receiver operating characteristics (ROC) curves with areas under the curve (AUC) were plotted as performance metrics for both methods. RESULTS: Among 136 patients (45 and 91 patients from our institution and test set 2, respectively), 17 patients (11.2 %) had IDH1 mutations. The mean DSC of test set 1 was 0.78 ±â€¯0.14 (range, 0.34-0.94). A subset of 9 all-relevant features (8.4 %, 9/107) was selected. V-Net segmentation of the test set 2 yielded similar performance in predicting IDH1 mutation as compared to manual segmentation (V-Net AUC = 0.86 vs. manual AUC = 0.90). The optimal cut-point threshold of AUC yielded 86.8 % accuracy for manual segmentation and 75.8 % for V-Net segmentation. CONCLUSIONS: V-Net showed robust segmentation capability of glioblastoma on T2-weighted MRI. All-relevant radiomics features from both segmentation methods yielded a similar performance in IDH1 prediction.

Differential expression of MicroRNAs in patients with glioblastoma after concomitant chemoradiotherapy.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, and notorious for resistance to chemoradiotherapy. MicroRNAs (miRNAs) are significantly involved in the initiation and progression of numerous cancers; however, the role of miRNAs in recurrence of tumors remains unknown. Here we tried to identify novel miRNAs that are differentially expressed in recurrent GBM. Tissue samples were obtained from patients with primary and recurrent GBM treated with chemoradiotherapy, and the expression changes of miRNAs were measured by microarray. A total of 318 miRNAs were expressed in the GBM patients. The expression of 43 miRNAs were significantly altered at least 2-fold in primary and recurrent GBMs. Bioinformatic analysis revealed that the differentially expressed miRNAs and their putative target genes were mainly involved in cell death, cellular development, and cellular growth and proliferation, which are the key regulators for stem cells. Pathway analysis supported that the miRNAs may regulate signaling associated with induction and maintenance of cancer and stem cell, such as p53, ErbB1, Notch, Wnt, and TGF-ß signaling pathways. These data suggest that, in recurrent GBM, growth factor and anti-apoptotic signalings for cancer cell growth and proliferation are regulated by miRNAs. Our findings will aid future research in understanding the pathophysiology of recurrent GBM and identifying diagnostic markers and/or therapeutic targets for recurrence of GBM.