Monitoring breast cancer treatment using a Fourier transform infrared spectroscopy-based computational model.

Breast cancer affects one in four women, therefore, the search for new diagnostic technologies and therapeutic approaches is of critical importance. This involves the development of diagnostic tools to facilitate the detection of cancer cells, which is useful for assessing the efficacy of cancer therapies. One of the major challenges for chemotherapy is the lack of tools to monitor efficacy during the course of treatment. Vibrational spectroscopy appears to be a promising tool for such a purpose, as it yields Fourier transformation infrared (FTIR) spectra which can be used to provide information on the chemical composition of the tissue. Previous research by our group has demonstrated significant differences between the infrared spectra of healthy, cancerous and post-chemotherapy breast tissue. Furthermore, the results obtained for three extreme patient cases revealed that the infrared spectra of post-chemotherapy breast tissue closely resembles that of healthy breast tissue when chemotherapy is effective (i.e., a good therapeutic response is achieved), or that of cancerous breast tissue when chemotherapy is ineffective. In the current study, we compared the infrared spectra of healthy, cancerous and post-chemotherapy breast tissue. Characteristic parameters were designated for the obtained spectra, spreading the function of absorbance using the Kramers-Kronig transformation and the best fit procedure to obtain Lorentz functions, which represent components of the bands. The Lorentz function parameters were used to develop a physics-based computational model to verify the efficacy of a given chemotherapy protocol in a given case. The results obtained using this model reflected the actual patient data retrieved from medical records (health improvement or no improvement). Therefore, we propose this model as a useful tool for monitoring the efficacy of chemotherapy in patients with breast cancer.