Dr. Vinay Varadan Profile

Dr. Vinay Varadan

at Case Western Reserve Univ

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Publications (2)

Proceedings Article | 13 March 2019 Paper

Radiogenomic characterization of response to chemo-radiation therapy in glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways

Niha Beig, Prateek Prasanna, Virginia Hill, Ruchika Verma, Vinay Varadan, Anant Madabhushi, Pallavi Tiwari

Proceedings Volume 10950, 109501B (2019) https://doi.org/10.1117/12.2512258

KEYWORDS: Machine learning, Magnetic resonance imaging, Tumors, Cell death, Cancer, Resistance, Oncology, Feature extraction, Medical research, Brain, Radiotherapy

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Over 40% of Glioblastoma (GBM) patients do not respond to conventional chemo-radiation therapy (chemo-RT) and relapse within 6-9 months, suggesting that they may have been better suited for other targeted therapies. Currently, there are no biomarkers that can reliably predict patients' response to chemo-RT in GBM. We seek to evaluate the role of radiomic markers on pre-treatment MRI to predict GBM patients' response to chemo-RT. Further, to establish a biological underpinning of the radiomic markers, we identified radiogenomic correlates of the radiomic markers with signaling pathways that are known to impact chemo-RT response. A total of 49 studies with Gd-T1w, T2w, FLAIR MRI protocols and corresponding gene expression were obtained from Ivy GAP (n=29) and TCIA (n=20) databases. Responders (n=22) were patients with progression-free survival (PFS) of at least ≥ 6 months, while non-responders (n=27) had PFS < 6 months. 13 molecular pathways were curated from the MSigDB Hallmark gene set. For each study, enhancing tumor on MRI was manually segmented by an expert reader. 1390 3D-radiomic features (Gabor, Haralick, and Laws energy) were extracted from this region across all MRI protocols. Joint mutual information identified the 3 most predictive radiomic features in the training set (n=29). This was followed by correlating these features with the gene set enrichment analysis (GSEA) score computed for every pathway. A support vector machine (SVM) classifier was trained using these 3 features and validated on a test set (n=20) that resulted in an Area Under Curve (AUC) of 0.71 to distinguish chemo-RT responders from non-responders. Laws energy descriptor (characterizing appearance of edges, spots, and ripples) from the enhancing region on Gd-T1w MR images were found to best predict chemo-RT response. Radiogenomic correlation with GSEA scores revealed that these radiomic features were significantly associated with PI3K/AKT/mTOR (promotes cell proliferation, survival) and apoptosis (programmed cell death) signaling pathways (p < 0.03, False Discovery Rate = 5%).

Proceedings Article | 3 March 2017 Presentation + Paper

Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma

Niha Beig, Jay Patel, Prateek Prasanna, Sasan Partovi, Vinay Varadan, Anant Madabhushi, Pallavi Tiwari

Proceedings Volume 10134, 101341U (2017) https://doi.org/10.1117/12.2255694

KEYWORDS: Hypoxia, Magnetic resonance imaging, Brain, Cancer, Tumors, Statistical analysis, Feature extraction, Oxygen, Positron emission tomography

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Glioblastoma Multiforme (GBM) is a highly aggressive brain tumor with a median survival of 14 months. Hypoxia is a hallmark trait in GBM that is known to be associated with angiogenesis, tumor growth, and resistance to conventional therapy, thereby limiting treatment options for GBM patients. There is thus an urgent clinical need for non-invasively capturing tumor hypoxia in GBM towards identifying a subset of patients who would likely benefit from anti-angiogenic therapies (bevacizumab) in the adjuvant setting. In this study, we employed radiomic descriptors to (a) capture molecular variations of tumor hypoxia on routine MRI that are otherwise not appreciable; and (b) employ the radiomic correlates of hypoxia to discriminate patients with short-term survival (STS, overall survival (OS) < 7 months), mid-term survival (MTS) (7 months<OS<16 months), and long-term survival (LTS, OS>16 months). A total of 97 studies (25 STS, 36 MTS, 36 LTS) with Gadolinium T1-contrast (Gd-T1c), T2w, and FLAIR protocols with their corresponding gene expression profiles were obtained from the cancer genome atlas (TCGA) database. For each MRI study, necrotic, enhancing tumor, and edematous regions were segmented by an expert. A total of 30 radiomic descriptors (i.e. Haralick, Laws energy, Gabor) were extracted from every region across all three MRI protocols. By performing unsupervised clustering of the expression profile of hypoxia associated genes, a "low", "medium", or "high" index was defined for every study. Spearman correlation was then used to identify the most significantly correlated MRI features with the hypoxia index for every study. These features were further used to categorize each study as STS, MTS, and LTS using Kaplan-Meier (KM) analysis. Our results revealed that the most significant features (p < 0.05) were identified as Laws energy and Haralick features that capture image heterogeneity on FLAIR and Gd-T1w sequences. We also found these radiomic features to be significantly associated with survival, distinguishing MTS from LTS (p=.005) and STS from LTS (p=.0008).

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