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

• Published in: Academic radiology Vol. 24; no. 10; pp. 1233 - 1239
• Main Authors: Danala, Gopichandh, MS, Thai, Theresa, MD, Gunderson, Camille C., MD, Moxley, Katherine M., MD, Moore, Kathleen, MD, Mannel, Robert S., MD, Liu, Hong, PhD, Zheng, Bin, PhD, Qiu, Yuchen, PhD
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-10-2017
• Subjects: Aged; chemotherapy of ovarian cancer; Female; Humans; Image Processing, Computer-Assisted; Ovarian Neoplasms - diagnostic imaging; Ovarian Neoplasms - drug therapy; prediction efficacy of clinical trials; prediction of tumor response to chemotherapy; Quantitative image feature analysis; Radiology; radiomics; Retrospective Studies; ROC Curve; Tomography, X-Ray Computed - methods; prediction efficacy of clinical trials; Quantitative image feature analysis; radiomics; prediction of tumor response to chemotherapy; chemotherapy of ovarian cancer
• ISSN: 1076-6332; 1878-4046
• DOI: 10.1016/j.acra.2017.04.014

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.