Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer

We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unsta...

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Published in:	Laboratory investigation Vol. 92; no. 9; pp. 1358 - 1373
Main Authors:	Bird, Benjamin, Miljković, Milos̆, Remiszewski, Stan, Akalin, Ali, Kon, Mark, Diem, Max
Format:	Journal Article
Language:	English
Published:	New York Elsevier Inc 01.09.2012 Nature Publishing Group US Nature Publishing Group
Subjects:	631/1647/1407/1555 631/1647/527/2257 692/699/67/1612 692/700/139 artificial neural network analysis Biological and medical sciences Biotechnology Fundamental and applied biological sciences. Psychology histopathology Humans immunohistochemistry Investigative techniques, diagnostic techniques (general aspects) Laboratory Medicine Lung cancer Lung Neoplasms - classification Lung Neoplasms - diagnosis Medical sciences Medicine Medicine & Public Health Pathology Pneumology research-article spectral histopathology Spectrophotometry, Infrared - methods Tumors of the respiratory system and mediastinum lung cancer histopathology artificial neural network analysis immunohistochemistry spectral histopathology Immunohistochemistry Lung disease Biotechnology Respiratory disease Lung cancer 5: artificial neural network analysis Malignant tumor Neural network Anatomic pathology Histopathology Network analysis Clinical biology Classification Bronchus disease Infrared radiation Diagnosis Cancer Tool
ISSN:	0023-6837, 1530-0307, 1530-0307
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Abstract	We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry.
AbstractList	We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 μ m on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry. We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry.We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry. We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry. We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry.
Author	Bird, Benjamin Diem, Max Kon, Mark Miljković, Milos̆ Remiszewski, Stan Akalin, Ali
Author_xml	– sequence: 1 givenname: Benjamin surname: Bird fullname: Bird, Benjamin organization: Department of Chemistry and Chemical Biology, Laboratory for Spectral Diagnosis, Northeastern University, Boston, MA, USA – sequence: 2 givenname: Milos̆ surname: Miljković fullname: Miljković, Milos̆ organization: Department of Chemistry and Chemical Biology, Laboratory for Spectral Diagnosis, Northeastern University, Boston, MA, USA – sequence: 3 givenname: Stan surname: Remiszewski fullname: Remiszewski, Stan organization: Cireca Theranostics, Parsippany, NJ, USA – sequence: 4 givenname: Ali surname: Akalin fullname: Akalin, Ali organization: Department of Pathology, University of Massachusetts Medical School, Worcester, MA, USA – sequence: 5 givenname: Mark surname: Kon fullname: Kon, Mark organization: Department of Mathematics and Statistics, and Program in Bioinformatics, Boston University, Boston, MA, USA – sequence: 6 givenname: Max surname: Diem fullname: Diem, Max email: m.diem@neu.edu organization: Department of Chemistry and Chemical Biology, Laboratory for Spectral Diagnosis, Northeastern University, Boston, MA, USA
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Keywords	lung cancer histopathology artificial neural network analysis immunohistochemistry spectral histopathology Immunohistochemistry Lung disease Biotechnology Respiratory disease Lung cancer 5: artificial neural network analysis Malignant tumor Neural network Anatomic pathology Histopathology Network analysis Clinical biology Classification Bronchus disease Infrared radiation Diagnosis Cancer Tool
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Snippet	We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung...
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SubjectTerms	631/1647/1407/1555 631/1647/527/2257 692/699/67/1612 692/700/139 artificial neural network analysis Biological and medical sciences Biotechnology Fundamental and applied biological sciences. Psychology histopathology Humans immunohistochemistry Investigative techniques, diagnostic techniques (general aspects) Laboratory Medicine Lung cancer Lung Neoplasms - classification Lung Neoplasms - diagnosis Medical sciences Medicine Medicine & Public Health Pathology Pneumology research-article spectral histopathology Spectrophotometry, Infrared - methods Tumors of the respiratory system and mediastinum
Title	Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer
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