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Developing a new quantitative imaging marker to predict pathological complete response to neoadjuvant chemotherapy
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Paper Abstract

Neoadjuvant (NAT) chemotherapy is a standard treatment option for many breast cancer patients. Patients who achieved pathologic complete response (pCR) after NAT, usually have better prognosis than those without. Thus, prediction of pathologic response is an important clinical issue for breast cancer patient. The purpose of this study is to develop and analyze a new computer-aided detection (CAD) and machine learning scheme using the quantitative kinetic and texture based image features extracted from breast magnetic resonance imaging (MRI) performed before and after NAT chemotherapy to predict the pCR. The images of 153 breast cancer patients underwent NAT were included in the analytical dataset. Among them, 52 achieved pCR and 101 cases were non-pCR after the NAT. A CAD scheme was developed to segment breast region and compute a total of 38 kinetic and texture features from the segmented breast regions. An image feature reduction method was used to identify 8 optimal features from the original feature pool. Then, a fine Gaussian support vector machine (FGSVM) based classifier was used to classify the two categories of pCR and non-pCR cases, which were optimized and tested using a ten-fold cross validation method. The results indicated that using features extracted from the post-chemotherapy yielded the higher area under receiver operating characteristic curves (AUC) of 0.81±0.04 and accuracy of 82% compared to using pre-chemotherapy images. This study demonstrated that image features extracted from breast MR images acquired after the NAT chemotherapy have good potential in prediction of pathology complete response.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502O (13 March 2019); doi: 10.1117/12.2512655
Show Author Affiliations
Faranak Aghaei, The Univ. of Oklahoma (United States)
Alan B. Hollingsworth, Mercy Health Ctr. (United States)
Seyedehnafiseh Mirniaharikandehei, The Univ. of Oklahoma (United States)
Yunzhi Wang, The Univ. of Oklahoma (United States)
Hong Liu, The Univ. of Oklahoma (United States)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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