Infrared imaging technology for breast cancer detection – Current status, protocols and new directions. (May 2017)

• Record Type: Journal Article
• Authors: Kandlikar, Satish G.; Perez-Raya, Isaac; Raghupathi, Pruthvik A.; Gonzalez-Hernandez, Jose-Luis; Dabydeen, Donnette; Medeiros, Lori; Phatak, Pradyumna
• Is Part Of: International journal of heat and mass transfer. Volume 108:Part B(2017)
• Page Start: 2303
• Page End: 2320
• Publication Date: 2017-05
• Subjects: Thermal imaging -- Breast cancer -- Inverse modeling -- Bioheat modeling -- Thermography; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2017.01.086
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Accuracy of IR thermography in breast cancer detection is reviewed. An overview of the protocols and standards for IR thermography is presented. Inverse modeling technique can accurately determine size and position of malignant tumors. Artificial intelligence approaches used to distinguish breasts with a malignant tumor are discussed. Numerical models for breast cancer are analyzed and areas of improvement are recommended. Abstract: Early and accurate detection of breast cancer is a critical part of the strategy to reduce the morbidity and mortality associated with this common disease. While current guidelines recommend mammography for screening, the sensitivity and specificity of mammograms remains less than optimal, especially for patients with dense breast tissue. Thermography has been explored in the past as an alternative to mammography. Advances in IR cameras that are used to obtain thermal images of the breast as well as computational tools used to accurately model heat transfer within the breast have significantly increased the accuracy of thermography. The current work reviews the progress that has been made in using thermal imaging to detect breast cancer over the past three decades and identifies aspects that need further refinement for it to become a reliable tool to diagnose breast cancer. Recent advances and suggestions for future work in the field including using advanced simulation methods, inverse modeling, imaging protocols, and using artificial neural networks to better predict the location of the tumor are also presented.