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Proceedings Paper

Automated detection of breast tumor in MRI and comparison of kinetic features for assessing tumor response to chemotherapy
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Paper Abstract

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) is used increasingly in diagnosis of breast cancer and assessment of treatment efficacy in current clinical practice. The purpose of this preliminary study is to develop and test a new quantitative kinetic image feature analysis method and biomarker to predict response of breast cancer patients to neoadjuvant chemotherapy using breast MR images acquired before the chemotherapy. For this purpose, we developed a computer-aided detection scheme to automatically segment breast areas and tumors depicting on the sequentially scanned breast MR images. From a contrast-enhancement map generated by subtraction of two image sets scanned pre- and post-injection of contrast agent, our scheme computed 38 morphological and kinetic image features from both tumor and background parenchymal regions. We applied a number of statistical data analysis methods to identify effective image features in predicting response of the patients to the chemotherapy. Based on the performance assessment of individual features and their correlations, we applied a fusion method to generate a final image biomarker. A breast MR image dataset involving 68 patients was used in this study. Among them, 25 had complete response and 43 had partially response to the chemotherapy based on the RECIST guideline. Using this image feature fusion based biomarker, the area under a receiver operating characteristic curve is AUC = 0.850±0.047. This study demonstrated that a biomarker developed from the fusion of kinetic image features computed from breast MR images acquired pre-chemotherapy has potentially higher discriminatory power in predicting response of the patients to the chemotherapy.

Paper Details

Date Published: 20 March 2015
PDF: 11 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941429 (20 March 2015); doi: 10.1117/12.2081616
Show Author Affiliations
Faranak Aghaei, The Univ. of Oklahoma (United States)
Maxine Tan, The Univ. of Oklahoma (United States)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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