Breast cancer, the most common malignant disease with high mortality and morbidity, is the leading cause of death in women worldwide, especially in developing countries. The current methods for breast cancer diagnosis are time-consuming, expensive, and have low sensitivity, and these should be urgently addressed. Spectroscopy appears to offer a new method for clinical detection of normal and cancerous tissues. Fluorescence spectroscopy is a tool for the noninvasive acquisition of biochemical information and extracellular matrix, and its use is rapidly expanding owing to its safety and efficiency.
Breast tissue autofluorescence spectroscopy mostly uses a light source in the 420-512 nm band, and the tryptophan and tyrosine residues in the tissue are more sensitive to a laser with a wavelength of 405 nm. Therefore, this study used a 405 nm laser source to obtain autofluorescence spectra of breast tissue sections to avoid the interference of fat and blood on the spectrum during in vivo detection and to improve the spectrum intensity. Various biological macromolecules or molecular groups in human tissues emit fluorescence, and the fluorescence spectrum is a superimposed spectrum of various luminescent substances. Because of the short distance between the fluorescence wavelengths emitted by some fluorescent substances, a peak overlapping phenomenon occurs, and the spectral characteristic peaks are not easy to distinguish. On directly using the fluorescence spectral peak area for quantitative analysis, the quantification is neither objective nor accurate. Therefore, this study proposes a Gaussian function to fit the fluorescence spectrum of breast tissue slices. Because the Gaussian function describes the normal distribution, it effectively separates the overlapping peaks, simplifies the spectrum, and extracts information using the characteristic parameter peaks of the fitted spectrum. The area ratio method was used to analyze the differences in the spectra of the normal and cancerous breast tissues. Finally, after studying several samples, this paper proposes the use of an SVM algorithm to classify the fluorescence spectrum and analyzes the feasibility of this algorithm for the classification of the breast tissue fluorescence spectrum.
After the breast tissue becomes cancerous, the content of some fluorescent substances also changes. The normalized mean fluorescence spectra of normal and cancerous breast tissues show obvious red shifts in the fluorescence peaks of cancerous tissues relative to normal tissues (Fig. 4). The difference in fluorescent substances in normal and cancerous breast tissues was analyzed using area ratio method. The peak area ratios (A517/A492,A635/A492) of cancerous tissues are 2.4-8.5 times that of normal tissues, which can be considered as criteria for the diagnosis of breast cancer (Table 3). The discrimination results of the SVM algorithm are as follows: accuracy rate of 87.50%, recall rate of 93.94%, precision of 88.57%, and F1-score of 91.18%. This proves that the method has high sensitivity and recognition ability for the fluorescence spectral information of breast cancer tissue, and can map normal and cancerous tissue fluorescence spectral signatures in different states for classification.
In this study, the fluorescence spectrum of breast tissues under a 405 nm laser was collected. After fitting the fluorescence spectrum with a Gaussian function, the fluorescence spectrum peak area ratios for normal and cancerous tissues were calculated, and the effect of the SVM algorithm on the fluorescence spectrum was analyzed. The autofluorescence spectrum of cancerous breast tissues has a red-shift phenomenon compared with that of normal tissues; after separating the overlapping peaks by Gaussian function fitting fluorescence spectrum, the peak areas at 517 nm, 635 nm, and 492 nm of cancerous tissues have a significant ability to distinguish between normal and cancerous tissues. The SVM algorithm is feasible for the classification of breast tissue fluorescence spectrum, with an accuracy of 87.50%. This proves that the method can recall and pinpoint cancerous breast tissues, and has a strong balance between recall rate and precision, thus, providing a rapid diagnosis of breast cancer. In summary, the Gaussian function fitting the fluorescence spectrum can obtain the spectral characteristic information of the fluorophore in breast cancer and the peak area ratio can be used as the standard for diagnosing breast cancer. Moreover, the combination of the fluorescence spectrum and the SVM classification algorithm model can be used for multi-sample breast cancer, providing a feasible method for rapid diagnosis.
