A complementary approach in breast cancer diagnosis: Infrared thermal imaging
Infrared Physics and Technology, cilt.159, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 159
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.infrared.2026.106859
- Dergi Adı: Infrared Physics and Technology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chimica, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Breast cancer, Deep learning, Infrared thermal imaging (ITI), Machine learning, Thermal modeling, Tissue mimics
- Manisa Celal Bayar Üniversitesi Adresli: Evet
Özet
Breast cancer remains one of the most prevalent malignancies among women worldwide, and early detection is essential for improving survival and treatment outcomes. Although mammography, ultrasound, and magnetic resonance imaging are widely used clinically, their limitations have increased interest in complementary approaches. Depending on the modality, these include exposure to ionizing radiation, reduced sensitivity in dense breast tissue, operator dependence, and high cost. Infrared Thermal Imaging (ITI) has therefore emerged as a complementary modality because it is non-invasive, non-contact, radiation-free, and potentially cost-effective. Unlike anatomical imaging, ITI provides functional information by analyzing surface temperature distributions associated with physiological alterations in breast tissue. This review systematically examines ITI-based breast cancer research by organizing the literature into three complementary research environments—in silico, in vitro, and in vivo—thereby highlighting the distinct role of each stage in the development, validation, and clinical translation of ITI. In silico studies use mathematical models and computational simulations to investigate heat transfer in breast tissue and tumor-related temperature changes. In vitro studies use tissue-mimicking phantoms to test thermal models, imaging systems, and analysis methods under controlled laboratory conditions. In vivo studies represent the clinical application stage, where thermograms acquired from human subjects are analyzed using computer-aided methods for screening, diagnosis, and risk assessment. By integrating evidence across these environments, this review traces ITI from computational modeling through experimental validation to clinical application, clarifies its potential as a functional imaging technique that complements established breast imaging modalities, and provides a framework for future research.