Automated classification of breast pathology using local measures of broadband reflectance

Ashley M. Laughney; Venkataramanan Krishnaswamy; Pilar Beatriz García-Allende; Olga M. Conde; Wendy A. Wells; Keith D. Paulsen; Brian W. Pogue

doi:10.1117/1.3516594

1 November 2010 Automated classification of breast pathology using local measures of broadband reflectance

Ashley M. Laughney, Venkataramanan Krishnaswamy, Pilar Beatriz García-Allende, Olga M. Conde, Wendy A. Wells, Keith D. Paulsen, Brian W. Pogue

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Journal of Biomedical Optics, Vol. 15, Issue 6, 066019 (November 2010). https://doi.org/10.1117/1.3516594

Abstract

We demonstrate that morphological features pertinent to a tissue's pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-μm lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k-nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample pathology for microscopic assessment.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Ashley M. Laughney, Venkataramanan Krishnaswamy, Pilar Beatriz García-Allende, Olga M. Conde, Wendy A. Wells, Keith D. Paulsen, and Brian W. Pogue "Automated classification of breast pathology using local measures of broadband reflectance," Journal of Biomedical Optics 15(6), 066019 (1 November 2010). https://doi.org/10.1117/1.3516594

Published: 1 November 2010

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KEYWORDS

Tissues

Pathology

Scattering

Breast

Tissue optics

Reflectivity

Surgery

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