In spite of many efforts for early detection of breast cancer, there is still lack of technology for immediate
implementation. In the present study, the potential photoacoustic spectroscopy was evaluated in discriminating breast
cancer from normal, involving blood serum samples seeking early detection. Three photoacoustic spectra in time domain
were recorded from each of 20 normal and 20 malignant samples at 281nm pulsed laser excitations and a total of 120
spectra were generated. The time domain spectra were then Fast Fourier Transformed into frequency domain and
116.5625 - 206.875 kHz region was selected for further analysis using a combinational approach of wavelet, PCA and
logistic regression. Initially, wavelet analysis was performed on the FFT data and seven features (mean, median, area
under the curve, variance, standard deviation, skewness and kurtosis) from each were extracted. PCA was then
performed on the feature matrix (7x120) for discriminating malignant samples from the normal by plotting a decision
boundary using logistic regression analysis. The unsupervised mode of classification used in the present study yielded
specificity and sensitivity values of 100% in each respectively with a ROC - AUC value of 1. The results obtained have
clearly demonstrated the capability of photoacoustic spectroscopy in discriminating cancer from the normal, suggesting
its possible clinical implications.
The current study reports the photoacoustic spectroscopy-based assessment of breast tumor progression in a nude mice xenograft model. The tumor was induced through subcutaneous injection of MCF-7 cells in female nude mice and was monitored for 20 days until the tumor volume reached 1000 mm3. The tumor tissues were extracted at three different time points (days 10, 15, and 20) after tumor inoculation and subjected to photoacoustic spectral recordings in time domain ex vivo at 281 nm pulsed laser excitations. The spectra were converted into the frequency domain using the fast Fourier transformed tools of MATLAB® algorithms and further utilized to extract seven statistical features (mean, median, area under the curve, variance and standard deviation, skewness and kurtosis) from each time point sample to assess the tumor growth with wavelet principal component analysis based logistic regression analysis performed on the data. The prediction accuracies of the analysis for day 10 versus day 15, day 15 versus day 20, and day 10 versus day 20 were found to be 92.31, 87.5, and 95.2%, respectively. Also, receiver operator characteristics area under the curve analysis for day 10 versus day 15, day 15 versus day 20, and day 10 versus day 20 were found to be 0.95, 0.85, and 0.93, respectively. The ability of photoacoustic measurements in the objective assessment of tumor progression has been clearly demonstrated, indicating its clinical potential.