Literature DB >> 31081797

A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer.

Hong-Xia Zhang1, Zong-Qiong Sun2, You-Gen Cheng1, Guo-Qun Mao1.   

Abstract

PURPOSE: To explore the radiomics features of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC) based on X-ray mammography, and to differentiate the two groups of cases.
MATERIALS AND METHODS: Preoperative mammograms of 120 patients with breast ductal carcinoma confirmed by surgical pathology were retrospectively analyzed, which include 30 TNBC and 90 non-TNBC patients. The manual segmentation of breast lesions was performed by ITK-SNAP software and 12 radiomics features were extracted by Omni-Kinetics software. The differences of these radiomics features between TNBC and non-TNBC groups were compared, and the receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of each radiomics parameter for differentiating TNBC from non-TNBC, and the corresponding area under the curve (AUC), sensitivity and specificity were obtained.
RESULTS: There were statistically significant differences for 4 radiomics features between TNBC and non-TNBC datasets (P < 0.05). They were the roundness, concavity, gray average and skewness of breast lesions. Compared with non-TNBC, TNBC cases have following characteristics of (1) more round with the roundness of 0.621 vs. 0.413 (P < 0.001), (2) more regular with the concavity of 0.087 vs. 0.141 (P < 0.01), (3) higher density or gray average (67.261 vs. 56.842, P < 0.05), and (4) lower skewness (- 0.837 vs.- 0.671, P = 0.034). AUCs of ROC curves computed using features of the roundness and concavity were both larger than 0.70.
CONCLUSION: Radiomics features based on X-ray mammography may be helpful to distinguish between TNBC and non-TNBC, which were associated with breast tumor histology.

Entities:  

Keywords:  Triple negative breast cancer; X-ray mammography; evaluation of tumor characteristics; quantitative imaging markers

Year:  2019        PMID: 31081797     DOI: 10.3233/XST-180488

Source DB:  PubMed          Journal:  J Xray Sci Technol        ISSN: 0895-3996            Impact factor:   1.535


  13 in total

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Authors:  César Ortiz-Toro; Angel García-Pedrero; Mario Lillo-Saavedra; Consuelo Gonzalo-Martín
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Review 2.  The progress of multimodal imaging combination and subregion based radiomics research of cancers.

Authors:  Luyuan Zhang; Yumin Wang; Zhouying Peng; Yuxiang Weng; Zebin Fang; Feng Xiao; Chao Zhang; Zuoxu Fan; Kaiyuan Huang; Yu Zhu; Weihong Jiang; Jian Shen; Renya Zhan
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3.  Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients.

Authors:  Xuxin Chen; Wei Liu; Theresa C Thai; Tara Castellano; Camille C Gunderson; Kathleen Moore; Robert S Mannel; Hong Liu; Bin Zheng; Yuchen Qiu
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4.  Clinicopathologic breast cancer characteristics: predictions using global textural features of the ipsilateral breast mammogram.

Authors:  Ibrahem H Kanbayti; William I D Rae; Mark F McEntee; Ziba Gandomkar; Ernest U Ekpo
Journal:  Radiol Phys Technol       Date:  2021-06-02

5.  Identify the triple-negative and non-triple-negative breast cancer by using texture features of medicale ultrasonic image: A STROBE-compliant study.

Authors:  Qingyu Chen; Jianguo Xia; Jun Zhang
Journal:  Medicine (Baltimore)       Date:  2021-06-04       Impact factor: 1.817

6.  Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer.

Authors:  Hongwei Yu; Xianqi Meng; Huang Chen; Xiaowei Han; Jingfan Fan; Wenwen Gao; Lei Du; Yue Chen; Yige Wang; Xiuxiu Liu; Lu Zhang; Guolin Ma; Jian Yang
Journal:  Front Oncol       Date:  2020-04-15       Impact factor: 6.244

7.  Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis.

Authors:  Jinwoo Son; Si Eun Lee; Eun-Kyung Kim; Sungwon Kim
Journal:  Sci Rep       Date:  2020-12-09       Impact factor: 4.379

8.  Diagnostic Value of Radiomics Analysis in Contrast-Enhanced Spectral Mammography for Identifying Triple-Negative Breast Cancer.

Authors:  Yongxia Zhang; Fengjie Liu; Han Zhang; Heng Ma; Jian Sun; Ran Zhang; Lei Song; Hao Shi
Journal:  Front Oncol       Date:  2021-12-23       Impact factor: 6.244

Review 9.  Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease.

Authors:  Camil Ciprian Mireștean; Constantin Volovăț; Roxana Irina Iancu; Dragoș Petru Teodor Iancu
Journal:  J Clin Med       Date:  2022-01-26       Impact factor: 4.241

10.  A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer.

Authors:  Xian Jiang; Xiuhe Zou; Jing Sun; Aiping Zheng; Chao Su
Journal:  Contrast Media Mol Imaging       Date:  2020-08-25       Impact factor: 3.161

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