3D model of Tumor Habitat

Shape Changes between treatment effects and tumor recurrence

Radiation necrosis vs. tumor recurrence using radiomics on routine MRI

Survival prediction in Glioblastoma patients using radiomics analysis

Studying structural deformations in Glioblastoma to predict patient survival

Radiogenomic analysis of brain tumors

About Us

BrIC lab focuses on developing neuroinformatics techniques using machine learning, statistical modeling, and pattern recognition for applications in brain tumors and neurological disorders. One of the primary focuses of BrIC lab is to identify computerized image-based (also known as radiomic) phenotypes, and their associations with genomics (radiogenomics) and histo-pathology (radio-pathomics) for disease characterization.

Our vision is to conduct interdisciplinary and translational research in personalized diagnostics towards early diagnosis, prognosis, and response to treatment for  different neurological conditions including brain tumors. Through our clinical collaborations and research efforts, we aim to build technologies with a potential for near-term clinical impact in customizing personalized treatments and improving patient survival.

Our lab is located at Case Western Reserve University,  within School of Medicine and Case Comprehensive Cancer Center, and is affiliated with the Center for Computational Imaging and Personalized Diagnostics

 

Please check the research page for specific projects and research focus of our group.

June 30, 2020

BrIC lab in collaboration with Invent Lab awarded NCI ITCR U01 grant

BrIC in collaboration with Invent lab were recently awarded a NCI U01 grant for their project " RadxTools for assessing tumor treatment response on imaging".  The collaborative team will be developing new radiomics and AI tools to improve treatment for patients, allowing more precise understanding and prognosis of post-treated lesions.

July 23, 2020

BrIC lab paper accepted in Radiology-AI

Ruchika's paper entitled "Tumor-habitat Derived Radiomic Features on Pre-treatment MRI that are Prognostic of Progression-free Survival in Glioblastoma are Associated with Key Morphologic Attributes on Histopathology: A Feasibility Study", was recently accepted at Radiology-AI.

July 15, 2020

Pallavi named J&J Innovation Women in STEM2D Scholar

Pallavi was named 2020 J&J STEM2D Scholar for her contributions in the field of Technology. She was awarded for her cutting-edge research into the use of computational imaging, AI and machine learning to address critical clinical problems in brain tumors.

June 23, 2020

BrIC lab paper accepted at MICCAI 2020

Marwa's paper entitled "Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI", got accepted in MICCAI 2020 conference. 

May 06, 2020

Congrats to Niha for successfully defending her Ph.D. thesis

BrIC lab's PhD student Niha Beig successfully defended her Ph.D. thesis on radiogenomic models for GBM prognosis. Great job, Niha.

April 03, 2020

Congrats Sukanya for successfully defending her MS thesis defense

Congrats to Sukanya Iyer, MS student at BrIC lab, for successfully defending her thesis defense. Outstanding job, Sukanya. 

March 13, 2020

Work on quality assessment won accolades at SPIE

Our demo on quality assessment tools for medical imaging: MRQy and HistoQC won the Live Demonstration Certificate of Merit at the SPIE 2020 Medical Imaging Conference. The work was in collaboration with InVent lab led by Dr. Viswanath at CCIPD. 

February 11, 2020

BrIC lab paper awarded the "Most Cited Paper Award 2017"

BrIC lab paper entitled "Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings", was published in European Radiology in 2017, and has received 57 citations in the two years following publication (2018-2019). It was, therefore, awarded a "Most Cited Paper Award 2017" for receiving the second highest number of citations.

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