Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis

Shao-Hui Chuang; Xiaoyan Sun; Lisa Cazares; Julius Nyalwidhe; Dean Troyer; O. John Semmes; Jiang Li; Frederic D. McKenzie

doi:10.1117/12.844403

9 March 2010 Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis

Shao-Hui Chuang, Xiaoyan Sun, Lisa Cazares, Julius Nyalwidhe, Dean Troyer, O. John Semmes, Jiang Li, Frederic D. McKenzie

Author Affiliations +

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 762433 (2010) https://doi.org/10.1117/12.844403
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice.

Citation Download Citation

Shao-Hui Chuang, Xiaoyan Sun, Lisa Cazares, Julius Nyalwidhe, Dean Troyer, O. John Semmes, Jiang Li, and Frederic D. McKenzie "Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762433 (9 March 2010); https://doi.org/10.1117/12.844403

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