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Proceedings Paper

Novel computer-aided diagnosis of mesothelioma using nuclear structure of mesothelial cells in effusion cytology specimens
Author(s): Akif Burak Tosun; Oleksandr Yergiyev; Soheil Kolouri; Jan F. Silverman; Gustavo K. Rohde
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Paper Abstract

diagnostic standard is a pleural biopsy with subsequent histologic examination of the tissue demonstrating invasion by the tumor. The diagnostic tissue is obtained through thoracoscopy or open thoracotomy, both being highly invasive procedures. Thoracocenthesis, or removal of effusion fluid from the pleural space, is a far less invasive procedure that can provide material for cytological examination. However, it is insufficient to definitively confirm or exclude the diagnosis of malignant mesothelioma, since tissue invasion cannot be determined. In this study, we present a computerized method to detect and classify malignant mesothelioma based on the nuclear chromatin distribution from digital images of mesothelial cells in effusion cytology specimens. Our method aims at determining whether a set of nuclei belonging to a patient, obtained from effusion fluid images using image segmentation, is benign or malignant, and has a potential to eliminate the need for tissue biopsy. This method is performed by quantifying chromatin morphology of cells using the optimal transportation (Kantorovich–Wasserstein) metric in combination with the modified Fisher discriminant analysis, a k-nearest neighborhood classification, and a simple voting strategy. Our results show that we can classify the data of 10 different human cases with 100% accuracy after blind cross validation. We conclude that nuclear structure alone contains enough information to classify the malignant mesothelioma. We also conclude that the distribution of chromatin seems to be a discriminating feature between nuclei of benign and malignant mesothelioma cells.

Paper Details

Date Published: 20 March 2014
PDF: 6 pages
Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410Z (20 March 2014); doi: 10.1117/12.2043320
Show Author Affiliations
Akif Burak Tosun, Carnegie Mellon Univ. (United States)
Oleksandr Yergiyev, Allegheny General Hospital (United States)
Soheil Kolouri, Carnegie Mellon Univ. (United States)
Jan F. Silverman, Allegheny General Hospital (United States)
Gustavo K. Rohde, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 9041:
Medical Imaging 2014: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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