Technologies that can help make accurate, objective and automated decisions to aid the pathologist are sorely needed in clinical practice to improve accuracy and contain costs. The major goal of this project is to provide a practical imaging instrument for clinical and research use that can be operated by any trained person in pathology laboratories. The approach is based, first, on developing novel instrumentation and analytical methods for robust discrete frequency infrared (DF-IR) spectroscopic imaging. The instrument presents a departure from the current state of the art using a design inspired by recent advances in theory, newly-developed sources and advanced control algorithms. The integration of spectral analysis algorithms makes generation of molecular pathology data facile - without any dyes, stains or human supervision. The developed instrument and algorithms is tested in a clinical setting and results that will lead to clinical trials will be obtained. Finally, the approach is geared to bettr predict breast cancer course by utilizing the entire tumor microenvironment. As opposed to current practice, which largely relies only on epithelial cells, the new approach to using the entire tumor microenvironment has the potential to transform decision-making for patients and alter the standard practice of histologic assessment in research.

Public Health Relevance

The goal of this project is to provide a new chemical imaging instrument and methods for clinical and research use that can transform diagnosis without dyes, stains or human input. The designed instrument is high performance in terms of speed and signal to noise ratio, easily exceeding the state of the art. The instrument will be applied to address contemporary problems in breast cancer care and research by providing label-free diagnoses, stainless staining and tumor microenvironment based prognoses. If successful, establishment of the instrumentation and analytical methods here would alter the standard practice in histologic assessment of future research in cancer.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Study Section
Instrumentation and Systems Development Study Section (ISD)
Program Officer
Conroy, Richard
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University of Illinois Urbana-Champaign
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
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Wrobel, Tomasz P; Bhargava, Rohit (2018) Infrared Spectroscopic Imaging Advances as an Analytical Technology for Biomedical Sciences. Anal Chem 90:1444-1463
Mittal, Shachi; Yeh, Kevin; Leslie, L Suzanne et al. (2018) Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology. Proc Natl Acad Sci U S A 115:E5651-E5660
Mankar, Rupali; Walsh, Michael J; Bhargava, Rohit et al. (2018) Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging. Analyst 143:1147-1156
Pounder, F Nell; Reddy, Rohith K; Bhargava, Rohit (2016) Development of a practical spatial-spectral analysis protocol for breast histopathology using Fourier transform infrared spectroscopic imaging. Faraday Discuss 187:43-68
Bhargava, Rohit; Madabhushi, Anant (2016) Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology. Annu Rev Biomed Eng 18:387-412
Ostadhossein, Fatemeh; Misra, Santosh K; Mukherjee, Prabuddha et al. (2016) Defined Host-Guest Chemistry on Nanocarbon for Sustained Inhibition of Cancer. Small 12:5845-5861
Misra, Santosh K; Mukherjee, Prabuddha; Chang, Huei-Huei et al. (2016) Multi-functionality Redefined with Colloidal Carotene Carbon Nanoparticles for Synchronized Chemical Imaging, Enriched Cellular Uptake and Therapy. Sci Rep 6:29299
Yeh, Kevin; Kenkel, Seth; Liu, Jui-Nung et al. (2015) Fast infrared chemical imaging with a quantum cascade laser. Anal Chem 87:485-93
Gelber, Matthew K; Bhargava, Rohit (2015) Monolithic multilayer microfluidics via sacrificial molding of 3D-printed isomalt. Lab Chip 15:1736-41
Tiwari, Saumya; Bhargava, Rohit (2015) Extracting knowledge from chemical imaging data using computational algorithms for digital cancer diagnosis. Yale J Biol Med 88:131-43

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