A computational thermal model of breast cancer validated by clinical data
The present research study consisted of 1) a clinical study that collected data from female subjects with radiologic abnormalities associated with breast cancer, and 2) a computational thermal (or bioheat) modeling effort of breast cancer based on the data collected. The primary objective of this research was to construct an accurate thermal model of breast cancer, validated by real clinical data. The pilot clinical study enrolled eleven female subjects with radiologic breast abnormalities based on subjects’ routine diagnostic imaging procedures. Clinical data collected consisted of the following: High-resolution infrared (IR) images and video of subjects’ breasts; threedimensional (3D) breast surface scans of subjects’ breasts; and standard radiologic imaging data of subjects’ internal tumor definition (i.e., sizes and spatial locations within the breast). The clinical study received the appropriate Institutional Review Board (IRB) approval and informed consent was obtained from each subject prior to subjects’ enrollment in the study. The modeling effort aimed to construct a computational thermal model of the breast with cancer for a representative subject histologically diagnosed with breast cancer with the goal of quantifying the thermal characteristics of breast cancer, namely the blood perfusion rate and metabolic heat generation rate. IR images were used to validate the results of the thermal model, whereas 3D scans and radiologic imaging data served as geometric inputs to the model. Two modeling approaches were taken to model the cancerous breast: First, the “traditional” bioheat modeling approach, wherein blood perfusion and metabolic heat generation (or production) were considered; second, a novel and unique approach, herein referred to as “perfusion-driven modeling,” in which metabolic heat generation was not required to recreate surface temperatures obtained from infrared images, contrary to all prior modeling efforts. The motivation for implementing the latter modeling approach was the mathematical certainty of avoiding unrealistically high internal breast temperatures. Potential clinical applications for this modeling study of breast cancer include the following: Monitoring tumor response to cancer treatment over other imaging modalities; tracking tumor growth over time; parametrically simulating various tumor cases (i.e., size and location) in order to provide an approximation of expected breast temperatures; and simulating expected temperature distributions for hyperthermia and cryotherapy cancer treatments. The thermophysiology of breast cancer is surveyed, an extensive literature review of prior relevant clinical studies and thermal modeling efforts are outlined, data collected from the clinical study are presented, and finally modeling results using both approaches are presented and discussed.