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Proceedings Paper

Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis
Author(s): Binlin Wu; Xin Gao; Jason Smith; Jacob Bailin
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Paper Abstract

Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using a multivariate analysis algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved weights of the components, e.g. NADH and FAD are used to estimate the relative concentrations of the native fluorophores and the redox ratio. A machine learning algorithm such as support vector machine (SVM) is used for classification to distinguish normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. The classification performance is shown based on statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve. A cross validation method such as leave-one-out is used to evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting.

Paper Details

Date Published: 13 February 2017
PDF: 6 pages
Proc. SPIE 10038, Therapeutics and Diagnostics in Urology: Lasers, Robotics, Minimally Invasive, and Advanced Biomedical Devices, 100380U (13 February 2017); doi: 10.1117/12.2253517
Show Author Affiliations
Binlin Wu, Southern Connecticut State Univ. (United States)
Xin Gao, LaGuardia Community College (United States)
Jason Smith, Southern Connecticut State Univ. (United States)
Jacob Bailin, Southern Connecticut State Univ. (United States)


Published in SPIE Proceedings Vol. 10038:
Therapeutics and Diagnostics in Urology: Lasers, Robotics, Minimally Invasive, and Advanced Biomedical Devices
Hyun Wook Kang; Kin Foong Chan, Editor(s)

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