Hyperspectral imaging and quantitative analysis for prostate cancer detection

• Type of Material: Article
• Authors: Hamed Akbari, Emory University; Luma V. Halig, Emory University; David Schuster, Emory University; Adeboye Osunkoya, Emory University; Viraj Master, Emory University; Peter Nieh, Emory University; Georgia Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2012-07
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Final Published Version
• Copyright Statement: © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).
• Final Published Version (URL): http://dx.doi.org/10.1117/1.JBO.17.7.076005
• Title of Journal or Parent Work: Journal of Biomedical Optics
• ISSN: 1083-3668
• Volume: 17
• Issue: 7
• Start Page: 076005
• End Page: 076005
• Grant/Funding Information: This research is supported in part by NIH grant R01CA156775 (PI: Fei), Georgia Cancer Coalition Distinguished Clinicians and Scientists Award (PI: Fei), Emory Molecular and Translational Imaging Center (NIH P50CA128301), SPORE in Head and Neck Cancer (NIH P50CA128613), and the Atlanta Clinical and Translational Science Institute (ACTSI) that is supported by PHS Grant UL1 RR025008 from the Clinical and Translational Science Award program.
• Keywords: DIAGNOSIS; hyperspectral imaging; prostate cancer; Science & Technology; Biochemical Research Methods; MEANS CLASSIFICATION METHOD; Radiology, Nuclear Medicine & Medical Imaging; SUPPORT VECTOR MACHINES; MULTISCALE; SEGMENTATION; Physical Sciences; least squares support vector machine; Optics; Biochemistry & Molecular Biology; optical diagnosis; OPTICS; RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING; image classification; BIOCHEMICAL RESEARCH METHODS; CELLS; PHOTODYNAMIC THERAPY; RAMAN-SPECTROSCOPY; Life Sciences & Biomedicine; IMAGES; SURGERY
• Research Categories: Health Sciences, Pathology; Health Sciences, Oncology; Health Sciences, Radiology

Abstract

Hyperspectral imaging (HSI) is an emerging modality for various medical applications. Its spectroscopic data might be able to be used to noninvasively detect cancer. Quantitative analysis is often necessary in order to differentiate healthy from diseased tissue. We propose the use of an advanced image processing and classification method in order to analyze hyperspectral image data for prostate cancer detection. The spectral signatures were extracted and evaluated in both cancerous and normal tissue. Least squares support vector machines were developed and evaluated for classifying hyperspectral data in order to enhance the detection of cancer tissue. This method was used to detect prostate cancer in tumor-bearing mice and on pathology slides. Spatially resolved images were created to highlight the differences of the reflectance properties of cancer versus those of normal tissue. Preliminary results with 11 mice showed that the sensitivity and specificity of the hyperspectral image classification method are 92.8% to 2.0% and 96.9% to 1.3%, respectively. Therefore, this imaging method may be able to help physicians to dissect malignant regions with a safe margin and to evaluate the tumor bed after resection. This pilot study may lead to advances in the optical diagnosis of prostate cancer using HSI technology.