Format

Send to

Choose Destination
Acad Radiol. 2017 Oct;24(10):1233-1239. doi: 10.1016/j.acra.2017.04.014. Epub 2017 May 26.

Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

Author information

1
School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019.
2
Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma.
3
School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019. Electronic address: qiuyuchen@ou.edu.

Abstract

RATIONALE AND OBJECTIVES:

The study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients.

MATERIALS AND METHODS:

A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared.

RESULTS:

The highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively.

CONCLUSIONS:

This study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy.

KEYWORDS:

Quantitative image feature analysis; chemotherapy of ovarian cancer; prediction efficacy of clinical trials; prediction of tumor response to chemotherapy; radiomics

PMID:
28554551
PMCID:
PMC5875685
DOI:
10.1016/j.acra.2017.04.014
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center