2014 | OriginalPaper | Chapter
Predicting Triple-Negative Breast Cancer and Axillary Lymph Node Metastasis Using Diagnostic MRI
Authors : Jeff Wang, Fumi Kato, Kohsuke Kudo, Hiroko Yamashita, Hiroki Shirato
Published in: Breast Imaging
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.