Monitoring breast tumor progression by photoacoustic measurements: a xenograft mice model study

Abstract

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.

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo

“…The animals were regularly checked for tumor development, and the tumor progression was measured for length and breadth using a digital Vernier caliper. The tumor volume kinetics was calculated using the formula mentioned below [ 19 ]. Based on the allowed tumor volume limit after MCF 7 cells injection, the entire tumor growth period was divided into 4 assessment time points (day 5, 10, 15, and 20) and conducted the study considering day 0 as control.…”

“…In recent times, there has been tremendous progress achieved in the instrumentation and imaging applications by the technique [ 18 ]. The photoacoustic spectroscopy’s ability to detect biochemical alterations upon the onset of breast tumor development has been demonstrated [ 19 ]. In combination with machine learning algorithms, photoacoustic spectroscopy has shown tremendous improvement in the classification of abnormal tissues from healthy, signifying its diagnostic capability and possible alternative to currently available tools for disease diagnosis [ 20 ].…”

Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study

“…34 Additionally, no studies have reported protein conformational changes through photoacoustic (PA) spectroscopy studies. 35,36 In most analytical methods, including photoacoustic spectroscopy, the spectral patterns are influenced by the background noises that arise from the instrumentation and the surroundings, affecting the overall signal quality. Further, when the signal is weak, the background noise dominates over the actual signal.…”

Non-Invasive Monitoring of Human Health by Photoacoustic Spectroscopy

“…Rodrigues et al monitored the development of breast tumors in nude mice by utilizing PAS combined with support vector machine analysis [ 72 ]. Priya et al reported a method using wavelet principal component analysis-based logistic regression analysis to assess the tumor growth in mice [ 73 ]. Deng et al reviewed that PA imaging combined with deep learning can play an important role in cancer diagnosis and treatment [ 74 ].…”