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Proceedings Paper

Detection of cancer metastasis using a novel macroscopic hyperspectral method
Author(s): Hamed Akbari; Luma V. Halig; Hongzheng Zhang; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei
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Paper Abstract

The proposed macroscopic optical histopathology includes a broad-band light source which is selected to illuminate the tissue glass slide of suspicious pathology, and a hyperspectral camera that captures all wavelength bands from 450 to 950 nm. The system has been trained to classify each histologic slide based on predetermined pathology with light having a wavelength within a predetermined range of wavelengths. This technology is able to capture both the spatial and spectral data of tissue. Highly metastatic human head and neck cancer cells were transplanted to nude mice. After 2- 3 weeks, the mice were euthanized and the lymph nodes and lung tissues were sent to pathology. The metastatic cancer is studied in lymph nodes and lungs. The pathological slides were imaged using the hyperspectral camera. The results of the proposed method were compared to the pathologic report. Using hyperspectral images, a library of spectral signatures for different tissues was created. The high-dimensional data were classified using a support vector machine (SVM). The spectra are extracted in cancerous and non-cancerous tissues in lymph nodes and lung tissues. The spectral dimension is used as the input of SVM. Twelve glasses are employed for training and evaluation. The leave-one-out cross-validation method is used in the study. After training, the proposed SVM method can detect the metastatic cancer in lung histologic slides with the specificity of 97.7% and the sensitivity of 92.6%, and in lymph node slides with the specificity of 98.3% and the sensitivity of 96.2%. This method may be able to help pathologists to evaluate many histologic slides in a short time.

Paper Details

Date Published: 14 April 2012
PDF: 7 pages
Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 831711 (14 April 2012); doi: 10.1117/12.912026
Show Author Affiliations
Hamed Akbari, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Luma V. Halig, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Hongzheng Zhang, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Dongsheng Wang, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Zhuo Georgia Chen, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Baowei Fei, Winship Cancer Institute, Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8317:
Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

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