Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.

Purpose: Lower-grade gliomas (LGGs) are malignant tumors in young adults. Current therapy is associated with short-and long-term toxicity. Progression to higher tumor grade is associated with contrast enhancement on MRI. The majority of LGGs harbor mutations in the genes encoding isocitrate dehydrogenase 1 or 2 (IDH1/IDH2).Vorasidenib (AG-881) is a first-in-class, brain-penetrant, dual inhibitor of the mutant IDH1 and mutant IDH2 enzymes. Experimental Design:We conducted a multicenter, open-label, phase I, dose escalation study of vorasidenib in 93 patients with mutant IDH1/2 (mIDH1/2) solid tumors, including 52 patients with glioma that had recurred or progressed following standard therapy. Vorasidenib was administered orally, once daily, in 28-day cycles until progression or unacceptable toxicity. Enrollment is complete; this trial is registered with ClinicalTrials.gov, NCT02481154.Results: Vorasidenib showed a favorable safety profile in the glioma cohort. Doselimiting toxicities of elevated transaminases occurred at doses >100 mg and were reversible. The protocol-defined objective response rate per Response Assessment in Neuro-Oncology criteria for LGG (RANO-LGG) in patients with nonenhancing glioma was 18% (one partial response, three minor responses). The median progression-free survival was 36.8 months [95% confidence interval (CI), 11.2-40.8] for patients with nonenhancing glioma and 3.6 months (95% CI, 1.8-6.5) for patients with enhancing glioma. Exploratory evaluation of tumor volumes in patients with nonenhancing glioma showed sustained tumor shrinkage in multiple patients.Research.

Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma. Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients ( = 93) and orthotopic xenografts (OX; = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin ( expression levels. RNA and protein levels confirmed RNAi-mediated knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict expression status in patient, mouse, and interspecies. Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; = 02.021E-15). We determined causality between radiomic texture features and expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human-mouse matched coclinical trials.

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