Abstract
Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.
Highlights
Breast cancer is the most commonly diagnosed cancer among women in North America, and it is the second leading cause of cancer death in women.[1]
Even after controlling for tumor size, a similar statistically significant trend was observed within each size group (P = 0.04 for lesions size ⩽ 2 cm; P = 0.02 for lesions size 42 to ⩽ 5 cm) as with the entire data set for the relationship between magnetic resonance imaging (MRI) phenotype of enhancement texture and the cancer subtype (Figure 7)
The results from this study indicate that quantitative MRI analysis shows promise as a means for high-throughput image-based phenotyping in the discrimination of breast cancer subtypes
Summary
Breast cancer is the most commonly diagnosed cancer among women in North America, and it is the second leading cause of cancer death in women.[1] On the basis of receptor status, breast cancer can be classified into different subtypes. HER2positive breast cancers tend to be more aggressive and have a poorer prognosis than HER2-negative cancers, and ER-positive and PR-positive cases have lower risks of mortality compared with women with ER-negative and/or PR-negative disease.[2,3,4,5] Triplenegative (TN) cases (negative for all three receptors) relapse more quickly, and account for a large portion of breast cancer deaths after diagnosis.[2,3,4,6,7] By considering gene expression measurements, breast cancer can be categorized into several molecular subtypes, such as normal-like, luminal A, luminal B, HER2-enriched, and basal-like. Different molecular and receptor characterized subtypes have different prognoses and respond differently to specific therapies.[8,9,10,11,12]
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