Abstract
Early classification of breast cancers by molecular subtype allows for expeditious characterization of the disease and selection of appropriate treatment options. This ability is especially a concern for “triple-negative” cancers, which lack expression of the three cell surface receptors that most breast cancer hormonal therapies target, tend to be the most aggressive/metastatic compared to other subtypes, have lymph node involvement at diagnoses, and have relatively poor prognoses. In this study, we aim to develop predictive models using Dynamic Contrast-Enhanced (DCE) MRI-extracted features to identify triple-negative cancers and axillary lymph node metastasis at the time of diagnostic imaging. Using only morphological, pharmacokinetic, densitometric, statistical, textural, and textural kinetic features obtained from DCE-MRI, we were able to classify 91.3% of 69 lesions correctly for triple-negative status with a sensitivity of 55.6%, a specificity of 96.7, and an AUC of 0.889; 71.6% of lesions correctly for lymph node metastasis with a sensitivity of 50.0%, a specificity of 82.2%, and an AUC of 0.677.
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Wang, J., Kato, F., Kudo, K., Yamashita, H., Shirato, H. (2014). Predicting Triple-Negative Breast Cancer and Axillary Lymph Node Metastasis Using Diagnostic MRI. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_47
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DOI: https://doi.org/10.1007/978-3-319-07887-8_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
Online ISBN: 978-3-319-07887-8
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