Bino Varghese, PhD

Associate Professor Of Research Radiology

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Overview

Bino Varghese is an Assistant Professor of Research with expertise in imaging, image processing, quantification and biomechanics. He obtained his B.E. in Medical Electronics from Visveswariah Technological University in 2002 and his Master’s degree in Biomedical Engineering with a focus in imaging and image processing, from the Wright State University at Dayton, Ohio in 2005. In 2010, he received his Ph.D. in Biomedical Sciences from Wright State University. His doctoral dissertation was titled Quantitative Computed-Tomography Based Bone-Strength Indicators for the Identification of Low Bone-Strength Individuals in a Clinical Environment.

In Feb 2014, he joined the Department of Radiology at USC as a Research Laboratory Specialist. His current work focuses on investigating the technical feasibility and clinical value of quantifying multi-modal imaging biomarkers across various domains with an aim to maximize data utilization and increase clinical translation.

Awards

  • USC’s Mark and Mary Stevens Neuroimaging and Informatics Institute: Big Data to Knowledge Science Rotations for Advancing Discovery (RoAD-Trip) Award, 2017-2017

Publications

  • Conditional generative learning for medical image imputation Sci Rep. 2024 01 02; 14(1):171. . View in PubMed
  • Sarcopenia and body fat change as risk factors for radiologic incisional hernia following robotic nephrectomy Skeletal Radiol. 2023 Dec; 52(12):2469-2477. . View in PubMed
  • Investigating the role of imaging factors in the variability of CT-based texture analysis metrics J Appl Clin Med Phys. 2023 Nov 14; e14192. . View in PubMed
  • A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer Life (Basel). 2023 Oct 04; 13(10). . View in PubMed
  • Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma Oncology. 2023 Sep 12. . View in PubMed
  • Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach Mol Imaging Biol. 2023 08; 25(4):776-787. . View in PubMed
  • A cross-sectional study to test equivalence of low- versus intermediate-flip angle dynamic susceptibility contrast MRI measures of relative cerebral blood volume in patients with high-grade gliomas at 15 Tesla field strength. Front Oncol. 2023; 13:1156843. . View in PubMed
  • Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography Front Radiol. 2023; 3:1326831. . View in PubMed
  • Spatial assessments in texture analysis: what the radiologist needs to know Front Radiol. 2023; 3:1240544. . View in PubMed
  • Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study Oncology. 2023; 101(6):375-388. . View in PubMed
  • Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics J Ultrasound. 2022 Sep; 25(3):699-708. . View in PubMed
  • Radiomics quality score in renal masses: a systematic assessment on current literature Br J Radiol. 2022 Sep 01; 95(1137):20211211. . View in PubMed
  • An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology Diagnostics (Basel). 2022 May 30; 12(6). . View in PubMed
  • CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma Eur Radiol. 2022 Apr; 32(4):2552-2563. . View in PubMed
  • Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis Int J Mol Sci. 2022 Feb 25; 23(5). . View in PubMed
  • A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma Eur Urol Focus. 2022 07; 8(4):988-994. . View in PubMed
  • Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma Eur J Radiol Open. 2022; 9:100440. . View in PubMed
  • Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors Eur Radiol. 2021 Nov; 31(11):8522-8535. . View in PubMed
  • Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs Br J Radiol. 2021 Oct 01; 94(1126):20210221. . View in PubMed
  • Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom J Appl Clin Med Phys. 2021 Feb; 22(2):98-107. . View in PubMed
  • Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses Eur Radiol. 2021 Feb; 31(2):1011-1021. . View in PubMed
  • Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis J Digit Imaging. 2021 10; 34(5):1156-1170. . View in PubMed
  • Machine learning based predictors for COVID-19 disease severity Sci Rep. 2021 02 25; 11(1):4673. . View in PubMed
  • Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development Clin Imaging. 2020 Jul; 63:83-93. . View in PubMed
  • Myocardial Radiomics in Cardiac MRI AJR Am J Roentgenol. 2020 03; 214(3):536-545. . View in PubMed
  • Reliability of CT-based texture features: Phantom study J Appl Clin Med Phys. 2019 Aug; 20(8):155-163. . View in PubMed
  • Juxtatumoral perinephric fat analysis in clear cell renal cell carcinoma Abdom Radiol (NY). 2019 04; 44(4):1470-1480. . View in PubMed
  • Texture Analysis of Imaging: What Radiologists Need to Know AJR Am J Roentgenol. 2019 03; 212(3):520-528. . View in PubMed
  • Radiomics in Pulmonary Lesion Imaging AJR Am J Roentgenol. 2019 03; 212(3):497-504. . View in PubMed
  • Computed tomography-based texture analysis of bladder cancer: differentiating urothelial carcinoma from micropapillary carcinoma Abdom Radiol (NY). 2019 01; 44(1):201-208. . View in PubMed
  • Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Sci Rep. 2019 02 07; 9(1):1570. . View in PubMed
  • Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT Br J Radiol. 2018 Sep; 91(1089):20170789. . View in PubMed
  • Incidental Detection of Meningioma by 18F-FMAU PET/CT in a Patient With Suspected Prostate Cancer Clin Nucl Med. 2018 Jul; 43(7):e245-e246. . View in PubMed
  • Quantitative Contour Analysis as an Image-based Discriminator Between Benign and Malignant Renal Tumors Urology. 2018 Apr; 114:121-127. . View in PubMed
  • Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping AJR Am J Roentgenol. 2018 12; 211(6):W288-W296. . View in PubMed
  • A Decision-Support Tool for Renal Mass Classification J Digit Imaging. 2018 12; 31(6):929-939. . View in PubMed
  • Voxel-based whole-lesion enhancement parameters: a study of its clinical value in differentiating clear cell renal cell carcinoma from renal oncocytoma Abdom Radiol (NY). 2017 02; 42(2):552-560. . View in PubMed
  • Truncating mutation in the autophagy gene UVRAG confers oncogenic properties and chemosensitivity in colorectal cancers Nat Commun. 2015 Aug 03; 6:7839. . View in PubMed
  • A microfluidic technique to probe cell deformability J Vis Exp. 2014 Sep 03; (91):e51474. . View in PubMed