University of Illinois Urbana-Champaign

Breast cancer diagnosis using Fourier transform infrared imaging and statistical learning

Mittal, Shachi

Content Files
MITTAL-DISSERTATION-2019.pdf

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https://hdl.handle.net/2142/105925

Description

Title
Breast cancer diagnosis using Fourier transform infrared imaging and statistical learning
Author(s)
Mittal, Shachi
Issue Date
2019-07-11
Director of Research (if dissertation) or Advisor (if thesis)
Bhargava, Rohit
Doctoral Committee Chair(s)
Bhargava, Rohit
Committee Member(s)
  • Pan, Dipanjan
  • Balla, Andre Kajdacsy
  • Smith, Andrew
Department of Study
Bioengineering
Discipline
Bioengineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2019-11-26T20:59:42Z
Keyword(s)
  • Breast Pathology
  • Machine Learning, Infrared Spectroscopic Imaging
Abstract
Cancer alters both the morphological and the biochemical properties of multiple cell types in a tissue. Generally, the morphology of epithelial cells is practically used for routine disease diagnoses. Current histopathological diagnosis involves manual interpretation of stained images for patient diagnosis. This is prone to inter- observer variability leading to low concordance rates amongst pathologists. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited. To overcome these challenges, digital analysis of these images is suggested that can result in the determination of precise and quantitative metrics both for epithelial and stromal disease signatures. In my dissertation work, I focused on building combinatorial approaches using chemical imaging, histopathology images, machine learning and deep learning. An emerging area of investigation is using spectrometry to perform tissue analysis that utilizes chemical imaging coupled to machine learning to identify spectral signatures indicative of disease state and its progression. Infrared spectroscopic imaging biochemically characterizes breast cancer, both for the epithelial cells and the tumor-associated microenvironment. I utilized multiple breast tissue assignments and a supervised learning approach to create different histologic and pathologic models using both high definition (HD) and standard definition (SD) data. The comparison of HD and SD modalities shows that new information richness associated with better spatial resolution facilitates the creation of complex, multiclass models of breast tissue without compromising on the sensitivity and the specificity of tissue segmentation. These models were then extended to discrete frequency measurements for rapid analysis cutting down tissue analysis time from days to minutes, making the technology feasible for research optimizations and clinical translation. Additionally, I optimized and tuned existing convolutional neural networks to identify different disease states in breast cancer and the corresponding microenvironment. Finally, I developed analytical tools for early detection and standardized analysis of stained image data. This can offer new opportunities for objective, accurate and comprehensive patient diagnosis and prognostics.
Graduation Semester
2019-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/105925
Copyright and License Information
Copyright 2019 Shachi Mittal

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Dissertations and Theses - Bioengineering
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Graduate Theses and Dissertations at Illinois