Share Email Print
cover

Proceedings Paper

Quantitative diffusion weighted imaging parameters in tumor and peritumoral stroma for prediction of molecular subtypes in breast cancer
Author(s): Ting He; Ming Fan; Peng Zhang; Hui Li; Juan Zhang; Guoliang Shao; Lihua Li
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Breast cancer can be classified into four molecular subtypes of Luminal A, Luminal B, HER2 and Basal-like, which have significant differences in treatment and survival outcomes. We in this study aim to predict immunohistochemistry (IHC) determined molecular subtypes of breast cancer using image features derived from tumor and peritumoral stroma region based on diffusion weighted imaging (DWI). A dataset of 126 breast cancer patients were collected who underwent preoperative breast MRI with a 3T scanner. The apparent diffusion coefficients (ADCs) were recorded from DWI, and breast image was segmented into regions comprising the tumor and the surrounding stromal. Statistical characteristics in various breast tumor and peritumoral regions were computed, including mean, minimum, maximum, variance, interquartile range, range, skewness, and kurtosis of ADC values. Additionally, the difference of features between each two regions were also calculated. The univariate logistic based classifier was performed for evaluating the performance of the individual features for discriminating subtypes. For multi-class classification, multivariate logistic regression model was trained and validated. The results showed that the tumor boundary and proximal peritumoral stroma region derived features have a higher performance in classification compared to that of the other regions. Furthermore, the prediction model using statistical features, difference features and all the features combined from these regions generated AUC values of 0.774, 0.796 and 0.811, respectively. The results in this study indicate that ADC feature in tumor and peritumoral stromal region would be valuable for estimating the molecular subtype in breast cancer.

Paper Details

Date Published: 6 March 2018
PDF: 8 pages
Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790P (6 March 2018); doi: 10.1117/12.2293049
Show Author Affiliations
Ting He, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Peng Zhang, Hangzhou Dianzi Univ. (China)
Hui Li, 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)

© SPIE. Terms of Use
Back to Top