Automatic Determination of Hormone Receptor Status in Breast Cancer Using Thermography

摘要

Estrogren and progesterone hormone receptor status play a role in the treatment planning and prognosis of breast cancer. These are typically found after Immuno-Histo-Chemistry (IHC) analysis of the tumor tissues after surgery. Since breast cancer and hormone receptor status affect thermographic images, we attempt to estimate the hormone receptor status before surgery through non-invasive thermographic imaging. We automatically extract novel features from the thermographic images that would differentiate hormone receptor positive tumors from hormone receptor negative tumors, and classify them though machine learning. We obtained a good accuracy of 82 % and 79 % in classification of HR+ and HR tumors, respectively, on a dataset consisting of 56 subjects with breast cancer. This shows a novel application of automatic thermographic classification in breast cancer prognosis.