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Developing a quantitative ultrasound image feature analysis scheme to assess tumor treatment efficacy using a mouse model
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Paper Abstract

In order to improve the efficacy of cancer treatment, many new therapy methods have been proposed and tested. The purpose of this study is to investigate the feasibility and potential advantages of using a low-cost, portable and easy-touse ultrasound imaging modality to quantitatively assess treatment efficacy and/or identify optimal treatment methods. For this purpose, we developed a new interactive computer-aided detection (CAD) scheme based image segmentation and feature analysis scheme, which extracts quantitative image features from ultrasound images of athymic nude mice embedded with tumors. Twenty-three mice were involved in this study and treated using 7 different thermal therapy methods. The longitudinal ultrasound images of mice were taken pre- and post-treatment after 3-days of tumor embedment. A graphic user interface (GUI) of the CAD scheme allows manual segmentation of the tumor regions depicting on the images. Two CAD-computed tumor image feature pools were then established including the features computed from (1) pre-treatment images only and (2) difference between post- and pre-treatment images. Through data analysis, a number of top image features were identified to predict the effectiveness of treatment methods. Pearson Correlation coefficients between two top features selected from above two feature pools versus tumor size increase ratio were 0.373 and 0.552, respectively. Using an equally weighted fusion method of the top two features computed from pre- and post-treatment images, correlation coefficient increased to 0.679. Study results demonstrated the feasibility of extracting a new quantitative imaging marker from ultrasound images to assist in the evaluation of treatment efficacy or tumor response to the treatment.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 109551A (15 March 2019); doi: 10.1117/12.2512581
Show Author Affiliations
Seyedehnafiseh Mirniaharikandehei, The Univ. of Oklahoma (United States)
Joshua VanOsdol, Ctr. for Veterinary Health Science, Oklahoma State Univ. (United States)
Morteza Heidari, The Univ. of Oklahoma (United States)
Gopichandh Danala, The Univ. of Oklahoma (United States)
Ashish Ranjan, Ctr. for Veterinary Health Science, Oklahoma State Univ. (United States)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 10955:
Medical Imaging 2019: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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