Detectability of Breast Cancer Through Inverse Heat Transfer Modeling Using Patient-Specific Surface Temperatures

Abstract

Abstract Breast cancer in women is a prevalent disease that takes over 680,000 lives each year worldwide. Early detection of breast cancer through screening has played a significant role in reducing the mortality rates. The current screening paradigm has shown the difficulties in detecting cancers for patients with dense breasts, small and deep tumors, and cancer types that are difficult to visualize. Infrared imaging (IRI) aided by advanced thermal analysis of the breast has shown great promise in detecting cancer using surface temperatures effected by a metabolically active and highly perfused tumor region. We previously developed an inverse heat transfer approach to detect the presence and absence of breast cancer using IRI, called the IRI-Numerical Engine. It was validated with 23 biopsy-proven breast cancer patients irrespective of breast density and cancer type at various tumor depths (0.95 cm–5.45 cm from the breast surface). The current work is aimed to obtain the detectability limit of the IRI-Numerical Engine by testing the capability of detecting 10–20 mm tumors at various depths in patient-specific digital breast models (DBMs). In addition, a study on the effect of tumor size, tumor location, breast shape, and breast size on the surface temperatures of patient-specific models was conducted to verify that an IR camera could capture these surface temperature distributions. The algorithm was able to detect the presence of a tumor at various depths, and deep tumors are detectable given the appropriate thermal sensitive IR camera.