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Proceedings Paper

Analysis of DCE-MRI features in tumor and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer
Author(s): Hui Li; Ming Fan; Peng Zhang; Yuanzhe Li; Hu Cheng; Juan Zhang; Guoliang Shao; Lihua Li
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Paper Abstract

Breast cancer, with its high heterogeneity, is the most common malignancies in women. In addition to the entire tumor itself, tumor microenvironment could also play a fundamental role on the occurrence and development of tumors. The aim of this study is to investigate the role of heterogeneity within a tumor and the surrounding stromal tissue in predicting the Ki-67 proliferation status of oestrogen receptor (ER)-positive breast cancer patients. To this end, we collected 62 patients imaged with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for analysis. The tumor and the peritumoral stromal tissue were segmented into 8 shells with 5 mm width outside of tumor. The mean enhancement rate in the stromal shells showed a decreasing order if their distances to the tumor increase. Statistical and texture features were extracted from the tumor and the surrounding stromal bands, and multivariate logistic regression classifiers were trained and tested based on these features. An area under the receiver operating characteristic curve (AUC) were calculated to evaluate performance of the classifiers. Furthermore, the statistical model using features extracted from boundary shell next to the tumor produced AUC of 0.796±0.076, which is better than that using features from the other subregions. Furthermore, the prediction model using 7 features from the entire tumor produced an AUC value of 0.855±0.065. The classifier based on 9 selected features extracted from peritumoral stromal region showed an AUC value of 0.870±0.050. Finally, after fusion of the predictive model obtained from entire tumor and the peritumoral stromal regions, the classifier performance was significantly improved with AUC of 0.920. The results indicated that heterogeneity in tumor boundary and peritumoral stromal region could be valuable in predicting the indicator associated with prognosis.

Paper Details

Date Published: 6 March 2018
PDF: 8 pages
Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 1057907 (6 March 2018); doi: 10.1117/12.2293047
Show Author Affiliations
Hui Li, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Peng Zhang, Hangzhou Dianzi Univ. (China)
Yuanzhe Li, Hangzhou Dianzi Univ. (China)
Hu Cheng, Hangzhou Dianzi Univ. (China)
Juan Zhang, Zhejiang Cancer Hospital (China)
Guoliang Shao, Zhejiang Cancer Hospital (China)
Lihua Li, Hangzhou Dianzi Univ. (China)

Published in SPIE Proceedings Vol. 10579:
Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
Jianguo Zhang; Po-Hao Chen, Editor(s)

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