Computational methods for cancer diagnosis and prognosis from FT-IR spectroscopy data

Kwak, Jin Tae

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https://hdl.handle.net/2142/42305 Copy

Description

Title

Computational methods for cancer diagnosis and prognosis from FT-IR spectroscopy data

Author(s)

Kwak, Jin Tae

Issue Date

2013-02-03T19:30:54Z

Director of Research (if dissertation) or Advisor (if thesis)

Sinha, Saurabh

Doctoral Committee Chair(s)

Sinha, Saurabh

Committee Member(s)

• Bhargava, Rohit
• Kajdacsy-Balla, André
• Han, Jiawei
• Ahuja, Narendra

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Prostate cancer
• Cancer diagnosis and prognosis
• Fourier transform infrared (FT-IR) spectroscopy imaging
• Chemical imaging
• Optical microscopy imaging
• Tissue microarray
• Histologic analysis

Abstract

Prostate cancer (PCa) is the single most prevalent cancer in US men. PCa diagnosis and prognosis are crucial processes in managing patients and disease. These greatly affect decision-making of treatment and post-treatment strategies. Manual histologic assessment of stained tissue forms gold standard of diagnosis and limits speed and accuracy in clinical practice and research of prostate cancer diagnosis. Outcome prediction by clinical, pathologic, imaging and computational tools outperforms manual ad hoc decisions, but the deficiencies in these tools are apparent and hamper effective and efficient treatment plans. Here, we sought to develop automated tools for cancer pathology using fourier transform infrared (FT-IR) spectroscopy imaging. We employ tissue microarrays (TMAs) to record IR imaging data and adopt elaborated computational and analytical algorithms and statistical methods to recognize patterns and to construct robust systems. We firstly suggest a statistical framework to examine FT-IR imaging from a large population data in appreciation of its design and to identify primary sources of variation. Secondly, we develop cancer detection method by combining FT-IR imaging with microcopy imaging. Extracted morphologic features are excellent in recognizing cancer tissue and robust to staining conditions. Thirdly, a novel decision-support system to aid cancer pathology is introduced and provides an easy access and maintenance of tissues. Lastly, we develop a new prognostic model utilizing FT-IR imaging where stromal chemical features are detected and utilized to characterize cancer progression. The computational and automated methods developed here will prove the utility of FT-IR imaging in cancer pathology and help the development of solid protocols for clinical translation, thereby advancing PCa pathology service today.

Graduation Semester

2012-12

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http://hdl.handle.net/2142/42305

Copyright and License Information

Copyright 2012 Jin Tae Kwak

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