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Proceedings Paper

Robust tumor morphometry in multispectral fluorescence microscopy
Author(s): Ali Tabesh; Yevgen Vengrenyuk; Mikhail Teverovskiy; Faisal M. Khan; Marina Sapir; Douglas Powell; Ricardo Mesa-Tejada; Michael J. Donovan; Gerardo Fernandez
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Paper Abstract

Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy images of prostate biopsy specimens. To enable feature extraction, EN and cytoplasm regions were first segmented from the IF images. Then, feature sets consisting of the characteristics of the minimum spanning tree (MST) connecting the EN and the fractal dimension (FD) of gland boundaries were obtained from the segmented compartments. We demonstrated the utility of the proposed features in prostate cancer recurrence prediction on a multi-institution cohort of 1027 patients. Univariate analysis revealed that both FD and one of the MST features were highly effective for predicting cancer recurrence (p ≤ 0.0001). In multivariate analysis, an MST feature was selected for a model incorporating clinical and image features. The model achieved a concordance index (CI) of 0.73 on the validation set, which was significantly higher than the CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice (p < 0.0001). The contributions of this work are twofold. First, it is the first demonstration of the utility of the proposed features in morphometric analysis of IF images. Second, this is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.

Paper Details

Date Published: 3 March 2009
PDF: 9 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726015 (3 March 2009); doi: 10.1117/12.812968
Show Author Affiliations
Ali Tabesh, Aureon Labs., Inc. (United States)
Yevgen Vengrenyuk, Aureon Labs., Inc. (United States)
Mikhail Teverovskiy, Aureon Labs., Inc. (United States)
Faisal M. Khan, Aureon Labs., Inc. (United States)
Marina Sapir, Aureon Labs., Inc. (United States)
Douglas Powell, Aureon Labs., Inc. (United States)
Ricardo Mesa-Tejada, Aureon Labs., Inc. (United States)
Columbia Univ. College of Physicians and Surgeons (United States)
Michael J. Donovan, Aureon Labs., Inc. (United States)
Gerardo Fernandez, Aureon Labs., Inc. (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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