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Proceedings Paper

Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation
Author(s): Abolfazl Zargari; Yue Du; Theresa C. Thai; Camille C. Gunderson; Kathleen Moore; Robert S. Mannel; Hong Liu; Bin Zheng; Yuchen Qiu
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Paper Abstract

The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.

Paper Details

Date Published: 19 February 2018
PDF: 6 pages
Proc. SPIE 10495, Biophotonics and Immune Responses XIII, 104950K (19 February 2018); doi: 10.1117/12.2290227
Show Author Affiliations
Abolfazl Zargari, The Univ. of Oklahoma (United States)
Yue Du, The Univ. of Oklahoma (United States)
Theresa C. Thai, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Camille C. Gunderson, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Kathleen Moore, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Robert S. Mannel, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Hong Liu, The Univ. of Oklahoma (United States)
Bin Zheng, The Univ. of Oklahoma (United States)
Yuchen Qiu, The Univ. of Oklahoma (United States)

Published in SPIE Proceedings Vol. 10495:
Biophotonics and Immune Responses XIII
Wei R. Chen, Editor(s)

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