Breast Cancer Screening Using Inverse Modeling of Surface Temperatures and Steady-State Thermal Imaging.

Simple Summary: Early cancer detection is crucial for favorable patient outcomes, with mammography playing a central role in breast cancer detection. However, challenges persist, such as limited sensitivity in dense breast tissue and low specificity leading to excessive invasive testing. Leveraging the distinct biological characteristics of malignant tumors, we employed high-sensitivity thermal imaging to identify temperature changes associated with cancer. This innovative approach integrates a novel imaging technique and a physics-based prediction model to accurately ascertain the presence of breast cancer, regardless of size, location, and breast density. Our goal is to develop this technique as a complementary tool to mammography for widespread screening and as a cost-effective, stand-alone method for specific populations underserved by mammography. Cancer is characterized by increased metabolic activity and vascularity, leading to temperature changes in cancerous tissues compared to normal cells. This study focused on patients with abnormal mammogram findings or a clinical suspicion of breast cancer, exclusively those confirmed by biopsy. Utilizing an ultra-high sensitivity thermal camera and prone patient positioning, we measured surface temperatures integrated with an inverse modeling technique based on heat transfer principles to predict malignant breast lesions. Involving 25 breast tumors, our technique accurately predicted all tumors, with maximum errors below 5 mm in size and less than 1 cm in tumor location. Predictive efficacy was unaffected by tumor size, location, or breast density, with no aberrant predictions in the contralateral normal breast. Infrared temperature profiles and inverse modeling using both techniques successfully predicted breast cancer, highlighting its potential in breast cancer screening. [ABSTRACT FROM AUTHOR]