Quick identification of prostate cancer by wavelet transform-based photoacoustic power spectrum analysis

Abstract

Pathology is currently the gold standard for grading prostate cancer (PCa). However, pathology takes considerable time to provide a final result and is significantly dependent on subjective judgment. In this study, wavelet transform-based photoacoustic power spectrum analysis (WT-PASA) was used for grading PCa with different Gleason scores (GSs). The tumor region was accurately identified via wavelet transform time-frequency analysis. Then, a linear fitting was conducted on the photoacoustic power spectrum curve of the tumor region to obtain the quantified spectral parameter slope . The results showed that high GSs have small glandular cavity structures and higher heterogeneity, and consequently, the slopes at both 1210 nm and 1310 nm were high ( p < 0.01). The classification accuracy of the PA time frequency spectrum (PA-TFS) of tumor region using ResNet-18 was 89% at 1210 nm and 92.7% at 1310 nm. Further, the testing time was less than 7 mins. The results demonstrated that identification of PCa can be rapidly and objectively realized using WT-PASA.

Biomedical Application of Photoacoustics: A Plethora of Opportunities

“…In a current study, wavelet transform-based PA spectral analysis was used for grading the prostate tumor based on Gleason scores (GSs). This study utilized 1210 nm and 1310'nm wavelengths to obtain PA frequency spectra, which delineated a high slope for high GSs [123]. In a current study, machine learning-assisted PA spectroscopy was utilized to identify prostate cancer.…”

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors

“…They further quantified the Gleason score of prostate cancer based on the tissue microscopic architecture using PASA. 45 Our group combined the PASA with machine leaning to better mine the data information and achieved a high diagnostic accuracy of prostate cancer, 46 – 49 osteoporosis, 50 and breast cancer. 51 , 52 PASA has shown considerable potential in evaluating the endogenous chromophore in biological tissues for tumor diagnosis.…”

Improving photoacoustic imaging in low signal-to-noise ratio by using spatial and polarity coherence

“…Images of optical absorption in the tissue can be reconstructed from PA waves detected by an ultrasound (US) transducer. Therefore, PAI combines rich optical contrast with good US spatial resolution in deep tissue [4] , [5] , [6] . PAI has demonstrated the potential in characterizing structural, functional, physiological, and molecular information for medical diagnosis [7] , [8] , [9] , [10] , [11] .…”