TY - GEN
T1 - Photoacoustic spectroscopy based investigatory approach to discriminate breast cancer from normal
T2 - Photonic Therapeutics and Diagnostics XI
AU - Priya, Mallika
AU - Rao, Bola Sadashiva Satish
AU - Chandra, Subhash
AU - Ray, Satadru
AU - Mathew, Stanley
AU - Datta, Anirbit
AU - Nayak, Subramanya G.
AU - Mahato, Krishna Kishore
N1 - Publisher Copyright:
© 2016 SPIE.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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U2 - 10.1117/12.2210900
DO - 10.1117/12.2210900
M3 - Conference contribution
AN - SCOPUS:84973392605
VL - 9689
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Photonic Therapeutics and Diagnostics XII
A2 - Mandelis, Andreas
A2 - Choi, Bernard
A2 - Wong, Brian J. F.
A2 - Ilgner, Justus F.
A2 - Marcu, Laura
A2 - Skala, Melissa C.
A2 - Kollias, Nikiforos
A2 - Zeng, Haishan
A2 - Kang, Hyun Wook
A2 - Tearney, Guillermo J.
A2 - Gregory, Kenton W.
A2 - Campagnola, Paul J.
PB - SPIE
Y2 - 7 February 2015 through 8 February 2015
ER -
