br Declaration of Competing Interest br The authors declare
Declaration of Competing Interest
“The authors declare no potential conflicts of interest”.
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9 Original Investigation
Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features
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.
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 sta-tistic, 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. 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 dioecious 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 < .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.
Key Words: Molecular subtypes; mammogram; radiomics; breast cancer.
© 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
B reast cancer subtyping has important therapeutic implications on clinical management of the disease. The major breast cancer molecular subtypes include
luminal A, luminal B, human epidermal growth factor recep-tor 2 (HER2)-enriched, and triple-negative types (1). In
© 2018 The Association of University Radiologists. Published by Elsevier Inc.
All rights reserved.
general, luminal tumors represent a majority (70%) of inva-sive breast cancers and respond well to endocrine therapy. HER2-enriched tumors are better candidates for targeted antibody therapy (2). Triple-negative cancers can be more aggressive and difficult to treat, but some of these tumors may respond well to chemotherapy (2,3). Breast cancer subtypes can be categorized by genetic array testing or approximated in typical clinical practice using immunohis-tochemistry markers. Immunohistochemistry requires tissue specimens typically obtained by a needle biopsy. Because of the relatively small tissue sample size and tumor heterogene-ity, the subtyping assessment performed on a needle biopsy sample may not be representative of the tumor entirety. Clin-ical imaging is a noninvasive approach that has the ability to capture a broader range of tumor heterogeneity than from a single tissue sample (4). Recently, breast cancer subtype char-acterization has made progress on using radiological images. For example, certain qualitative and visual information of the imaging characteristics assessed on breast magnetic resonance imaging (MRI), mammography, or ultrasound has been