Breast Tuberculosis (Tb) is a chronic granulomatous disease predominantly caused by Mycobacterium tuberculosis which is difficult to diagnose. The non-specific clinical and imaging characteristics and lack of familiarity of clinicians with this entity have led to increased rates of misdiagnosis as breast cancer or pyogenic breast abscess and make it a difficult diagnosis. Medical Infrared imaging is a non-invasive technology that records the temperature pattern of the skin by detecting emitted infrared radiation. This technology has been investigated as an aid in the detection and evaluation of several medical conditions such as psoriasis, burn wounds and breast cancer. In this work the thermal pattern of the breast of fourteen women with breast tuberculosis were obtained and compared to thermal patterns of healthy subjects and breast cancer patients. Results show that the average breast temperature in breast Tb patients is higher (34.0±0.7 °C) than the average temperature observed in healthy women (32.2±1.3 °C) but lower than the average temperature observed in breast cancer patients (35.8±1.6 °C). Also the thermal images of breast Tb did not present the characteristic vascularity patterns present in breast cancer thermal images. These results could be used for the implementation of digital infrared imaging as an adjunct method for differential diagnosis of breast pathologies.
Mammography, which is the most common screening technique for breast cancer, is known to be less sensitive for women with dense breast tissue. Breast thermography, which records an image from the temperature pattern at the surface of the skin has been used as an adjunct to detect breast pathologies. The working principle of breast thermography relies on the increase in vascularity and metabolic heat generation of breast tumors compared to healthy tissue, which generates an increase in temperature that can be detected by current infrared imaging equipment. In this work the effect of breast density on the temperature pattern of the breast is investigated through finite element simulations. A three-dimensional scan of a real patient was imported into COMSOL Multiphysics along with structures resembling breast lobes, by changing the volume ratio of breast lobes and the whole breast an approximation of different breast densities was simulated. The thermal parameters of the different breast tissues were obtained from the literature and finite element simulations of heat transfer in biological tissue was performed using COMSOL Multiphysics. Results show that the breast density does not impact significantly the temperature pattern of the breast, therefore it can be concluded that thermography does not dependent on breast density.
It has been widely reported that breast tumors produce surface temperature signatures due to an increased metabolic heat generation rate and angiogenesis (the generation of new blood vessels around a tumor). The present work provides an assessment of the feasibility of using sparse contact thermometry to detect tumors in the breast. The surface temperatures at positions approximately corresponding to the sensor locations in a proposed sensor array were obtained from infrared thermography images of 123 healthy patients and 27 patients with breast cancer. A Support Vector Machine was trained and tested through Leave One Out Cross Validation. The model obtained an AUC ROC of 0.914, with a sensitivity of 92.6% and specificity of 82.1%. These results are close to the gold standard and even higher in women with high breast density. The present work shows promise for sparse contact thermometry. It is imperative to conduct further research with larger sample sizes and with data collected with sparse contact thermometry devices to determine the effectiveness of the method.
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