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Proceedings Paper

Tumor microenvironment for follicular lymphoma: structural analysis for outcome prediction
Author(s): C. Senaras; M. Pennell; W. Chen; B. Sahiner; A. Shana’ah; A. Louissaint; R. P. Hasserjian; G. Lozanski; M. N. Gurcan
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Paper Abstract

Follicular Lymphoma (FL) is the second most common subtype of lymphoma in the Western World. In 2009, about 15,000 new cases of FL were diagnosed in the U.S. and approximately 120,000 patients were affected. Both the clinical course and prognosis of FL are variable, and at present, oncologists do not have evidence-based systems to assess risk and make individualized treatment choices. Our goal is to develop a clinically relevant, pathology-based prognostic model in FL utilizing a computer-assisted image analysis (CaIA) system to incorporate grade, tumor microenvironment, and immunohistochemical markers, thereby improving upon the existing prognostic models. Therefore, we developed an approach to estimate the outcome of the follicular lymphoma patients by analyzing the tumor microenvironment as represented by quantification of CD4, CD8, FoxP3 and Ki67 stains intra- and inter-follicular regions. In our experiments, we analyzed 15 patients, and we were able to correctly estimate the output for the 87.5% of the patient with no evidence of disease after the therapy/operation.

Paper Details

Date Published: 6 March 2018
PDF: 6 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 1058106 (6 March 2018); doi: 10.1117/12.2295284
Show Author Affiliations
C. Senaras, The Ohio State Univ. (United States)
M. Pennell, The Ohio State Univ. (United States)
W. Chen, U.S. Food and Drug Administration (United States)
B. Sahiner, U.S. Food and Drug Administration (United States)
A. Shana’ah, The Ohio State Univ. (United States)
A. Louissaint, Massachusetts General Hospital (United States)
R. P. Hasserjian, Massachusetts General Hospital (United States)
G. Lozanski, The Ohio State Univ. (United States)
M. N. Gurcan, Wake Forest Baptist Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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