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Proceedings Paper

Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study
Author(s): Guolin Wu; Ming Fan; Juan Zhang; Bin Zheng; Lihua Li
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Paper Abstract

Breast cancer is one of the most malignancies among women in worldwide. Neoadjuvant Chemotherapy (NACT) has gained interest and is increasingly used in treatment of breast cancer in recent years. Therefore, it is necessary to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NACT. Recent studies have highlighted the use of MRI for predicting response to NACT. In addition, molecular subtype could also effectively identify patients who are likely have better prognosis in breast cancer. In this study, a radiomic analysis were performed, by extracting features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunohistochemistry (IHC) to determine subtypes. A dataset with fifty-seven breast cancer patients were included, all of them received preoperative MRI examination. Among them, 47 patients had complete response (CR) or partial response (PR) and 10 had stable disease (SD) to chemotherapy based on the RECIST criterion. A total of 216 imaging features including statistical characteristics, morphology, texture and dynamic enhancement were extracted from DCE-MRI. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.923 (P = 0.0002) in leave-one-out crossvalidation. The performance of the classifier increased to 0.960, 0.950 and 0.936 when status of HER2, Luminal A and Luminal B subtypes were added into the statistic model, respectively. The results of this study demonstrated that IHC determined molecular status combined with radiomic features from DCE-MRI could be used as clinical marker that is associated with response to NACT.

Paper Details

Date Published: 13 March 2017
PDF: 8 pages
Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380E (13 March 2017); doi: 10.1117/12.2254239
Show Author Affiliations
Guolin Wu, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Juan Zhang, Zhejiang Cancer Hospital (China)
Bin Zheng, Hangzhou Dianzi Univ. (China)
Univ. of Oklahoma (United States)
Lihua Li, Hangzhou Dianzi Univ. (China)


Published in SPIE Proceedings Vol. 10138:
Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Tessa S. Cook; Jianguo Zhang, Editor(s)

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