Abstract
The aim of this study is to investigate the feasibility of identifying and applying quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. A computer-aided detection (CAD) scheme with a graphic user interface was developed to conduct tumor segmentation and image feature analysis. A dataset involving ultrasound images of 23 athymic nude mice bearing C26 mouse adenocarcinomas was assembled. These mice were divided into 7 treatment groups utilizing a combination of thermal and nanoparticle-controlled drug delivery. Longitudinal ultrasound images of mice were taken prior and post-treatment in day 3 and day 6. After tumor segmentation, CAD scheme computed image features and created four feature pools including features computed from (1) prior treatment images only and (2) difference between prior and post-treatment images of day 3 and day 6, respectively. To predict tumor treatment efficacy, data analysis was performed to identify top image features and an optimal feature fusion method, which have a higher correlation to tumor size increase ratio (TSIR) determined at Day 10. Using image features computed from day 3, the highest Pearson Correlation coefficients between the top two features selected from two feature pools versus TSIR were 0.373 and 0.552, respectively. Using an equally weighted fusion method of two features computed from prior and post-treatment images, the correlation coefficient increased to 0.679. Meanwhile, using image features computed from day 6, the highest correlation coefficient was 0.680. Study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies.
Highlights
Www.nature.com/scientificreports in monitoring and assessing tumor response and change of tissue characteristics prior and post-treatment[11,12]
The top 5 performed image features selected on Day 3 and Day 6 are different as shown in Table 3, which indicates that treatments have an impact on the change of tumor morphological and texture characteristics
The results show that image features contain increased discriminatory power or higher correlation coefficients as they approach the endpoint of Day 10 (i.e., Day 6 vs. Day 3) to predict treatment efficacy or outcome
Summary
Www.nature.com/scientificreports in monitoring and assessing tumor response and change of tissue characteristics prior and post-treatment[11,12]. The feasibility of developing or identifying new quantitative imaging markers computed from ultrasound to predict or assess cancer treatment efficacy at an early stage has not been investigated and validated to date. Based on the concept and scientific premise of Radiomics[13], the objective of this study is to test the feasibility of identifying and extracting new quantitative image features or markers computed from ultrasound images to predict efficacy of cancer treatment at an early stage. In order to achieve the study objective, we developed an interactive computer-aided detection (CAD) scheme with an easy-to-use graphic user interface (GUI) to process ultrasound images acquired from the colon carcinoma tumor bearing mice and treating with a variety of thermal therapies. Data analysis was performed to identify top image features and their fusion method to generate new quantitative imaging markers to predict and compare the efficacy of the thermal therapies under tests at an early stage
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