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Heterogeneity of tumor and its surrounding stroma on DCE-MRI and diffusion weighted imaging in predicting histological grade and lymph node status of breast cancer
Author(s): Qingmei Chen; Ming Fan; Peng Zhang; Lihua Li; Maosheng Xu
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Paper Abstract

Breast cancer histological grade and lymph node status are important in evaluating the prognosis of patients. This study aim to predict these factors by analyzing the heterogeneity of tumor and its adjacent stroma based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI). A dataset of 172 patients with surgically verified lymph node status (positive lymph nodes, n=62; negative lymph nodes, n=110) who underwent preoperative DCE-MRI and DWI examination was collected. Among them, 144 cases had available histological grade information, including 56 cases of low-grade (grade 1 and 2), and 88 samples of high-grade (grade 3). To this end, we identified six tumor subregions on DCE-MRI as well as the corresponding subregions on ADC according to their distances to the tumor boundary. The statistical and Haralick texture features were extracted in each subregion, based on which predictive models were built to predict histological grade and lymph node status in breast cancer. An area under a receiver operating characteristic curve (AUC) was computed with a leave-one-out cross-validation (LOOCV) method to assess each classifier’s performance. For histological grade prediction, the classifier using DCE-MRI features in the inner tumor achieved best performance among all the subregions with AUC of 0.859. For lymph node status, classifier based on DCE-MRI features from tumor subregion of proximal peritumoral stromal shell obtained highest AUC of 0.882 among all the regions. Furthermore, the predictions from DCE-MRI and DWI were fused, and the AUC value was increased to 0.895 for discriminating histological grade. Our results demonstrate that DCE-MRI and ADC imaging features are complementary in predicting histological grade in breast cancer.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109541G (15 March 2019); doi: 10.1117/12.2513095
Show Author Affiliations
Qingmei Chen, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Peng Zhang, Hangzhou Dianzi Univ. (China)
Lihua Li, Hangzhou Dianzi Univ. (China)
Maosheng Xu, Zhejiang Provincial Hospital of Traditional Chinese Medicine (China)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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