@inproceedings{663bf056e0a548d0972a7b731bacec0d,
title = "Radiomics analysis of MRI for predicting molecular subtypes of breast cancer in young women",
abstract = "Breast cancer in young women is commonly aggressive, in part because the proportion of high-grade, triple-negative (TN) tumor is too high. There are certain limitations in the detection of biopsies or surgical specimens which only select part of tumor sample tissue and ignore the possible heterogeneity of tumors. In clinical practice, MRI is used for the diagnosis of breast cancer. MRI-based radiomics is a developing approach that may provide not only the diagnostic value for breast cancer but also the predictive or prognostic associations between the images and biological characteristics. In this work, we used radiomics methods to analyze MR images of breast cancer in 53 young women, and correlated the radiomics data with molecular subtypes. The results indicated a significant difference between TN type and non-TN type of breast cancer in young women on the radiomics features based on T2-weighted MR images. This may be helpful for the identification of TN type and guiding the therapeutic strategies.",
author = "Qinmei Li and James Dormer and Priyanka Daryani and Deji Chen and Zhenfeng Zhang and Baowei Fei",
note = "Funding Information: This work was supported by NIH grants CA176684, CA156775, CA204254, and HL140135, and Department of Radiology, the Second Affiliated Hospital of Guangzhou Medical University. The content is solely the responsibility of the authors and does not necessarily reflect the official views of the National Institutes of Health.; Medical Imaging 2019: Computer-Aided Diagnosis ; Conference date: 17-02-2019 Through 20-02-2019",
year = "2019",
doi = "10.1117/12.2512056",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Kensaku Mori and Hahn, {Horst K.}",
booktitle = "Medical Imaging 2019",
}