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. 2019 Feb;26(2):196-201.
doi: 10.1016/j.acra.2018.01.023. Epub 2018 Mar 8.

Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features (VSports手机版)

Affiliations

Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features

Wenjuan Ma et al. Acad Radiol. 2019 Feb.

Abstract

Rationale and objectives: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. VSports手机版.

Materials and methods: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions. A set of 39 quantitative radiomic features, including morphologic, grayscale statistic, and texture features, were extracted from the segmented lesion area. Three binary classifications of the subtypes were performed: triple-negative vs non-triple-negative, HER2-enriched vs non-HER2-enriched, and luminal (A + B) vs nonluminal V体育安卓版. The Naive Bayes machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method was used to select the most predictive features for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve and accuracy. .

Results: The model that used the combination of both the craniocaudal and the mediolateral oblique view images achieved the overall best performance than using either of the two views alone, yielding an area under receiver operating characteristic curve (or accuracy) of 0. 865 (0. 796) for triple-negative vs non-triple-negative, 0. 784 (0. 748) for HER2-enriched vs non-HER2-enriched, and 0. 752 (0. 788) for luminal vs nonluminal subtypes. Twelve most predictive features were selected by the least absolute shrink age and selection operator method and four of them (ie, roundness, concavity, gray mean, and correlation) showed a statistical significance (P< V体育ios版. 05) in the subtype classification. .

Conclusions: Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings. VSports最新版本.

Keywords: Molecular subtypes; breast cancer; mammogram; radiomics V体育平台登录. .

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Conflict of interest statement

Declarations of interest: None

"V体育平台登录" Figures

Figure 1.
Figure 1.
The top 12 ranked radiomic imaging features selected in the CC or MLO view images. Four of them, i.e., roundness, concavity, gray mean, and correlation, are statistically significant in the difference among the subtypes.

References

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