Literature DB >> 29969646

Quantitative analysis of peri-tumor fat in different molecular subtypes of breast cancer.

Jeon-Hor Chen1, Yang Zhang2, Siwa Chan3, Ruey-Feng Chang4, Min-Ying Su2.   

Abstract

BACKGROUND AND PURPOSES: The aim of this study was to develop morphological analytic methods to analyze the tumor-fat interface and in different peritumoral shells away from the tumor, and to compare the results among three molecular subtypes of breast cancer.
MATERIALS AND METHODS: A total of 102 women (mean age 48.5 y/o) with solitary well-defined breast cancers were analyzed, including 46 human epidermal growth factor receptor 2 (HER2) (+), 46 HER2(-) hormonal receptor (HR) (+), and 10 triple negative (TN) breast cancers. The tumor lesion, the breast, the fibroglandular and fatty tissue were segmented using well-established methods. The whole breast fat percentage and the peri-tumor interface fat percentage were measured. Three shells (SH1, SH2, SH3) surrounding the convex hall of the three dimensional (3D) tumor were defined and in each shell the volumetric percentage of fat was calculated. The peri-tumor interface fat percentage and the volumetric percentage of fat in the three peri-tumoral shells were compared among different subtypes.
RESULTS: In the TN group, the fat percentage on the tumor boundary was 43 ± 20% and 78 ± 12% for two dimensional (2D) and 3D measurement, respectively, which were the highest among the three subtypes but not significantly different. The fat percentage in SH2 and SH3 in the TN group was 82 ± 7% and 85 ± 7%, which was significantly higher compared to the two other two subtypes. The results remained after controlling for the whole breast fat percentage.
CONCLUSIONS: This study provided a feasible method for quantitative analysis of peri-tumoral tissue characteristics. Because of small patient number, the finding that TN tumors had the highest peri-tumor fat content among the three subtypes needs to be further verified with a large cohort study.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 29969646      PMCID: PMC6684233          DOI: 10.1016/j.mri.2018.06.019

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  3 in total

1.  Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

Authors:  Yang Zhang; Jeon-Hor Chen; Kai-Ting Chang; Vivian Youngjean Park; Min Jung Kim; Siwa Chan; Peter Chang; Daniel Chow; Alex Luk; Tiffany Kwong; Min-Ying Su
Journal:  Acad Radiol       Date:  2019-01-31       Impact factor: 3.173

2.  The Tumor-Fat Interface Volume of Breast Cancer on Pretreatment MRI Is Associated with a Pathologic Response to Neoadjuvant Chemotherapy.

Authors:  Hwan-Ho Cho; Minsu Park; Hyunjin Park; Eun Sook Ko; Na Young Hwang; Young-Hyuck Im; Kyounglan Ko; Sung Hoon Sim
Journal:  Biology (Basel)       Date:  2020-11-10

3.  Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions.

Authors:  Simin Wang; Yuqi Sun; Ruimin Li; Ning Mao; Qin Li; Tingting Jiang; Qianqian Chen; Shaofeng Duan; Haizhu Xie; Yajia Gu
Journal:  Eur Radiol       Date:  2021-06-29       Impact factor: 5.315

  3 in total

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