Photoacoustic Spectrum Analysis for Quick Identification and Grading of Prostate Cancer

Wu, Shiying; Chen, Yingna; Huang, Shengsong; Xu, Chengdang; Wu, Denglong; Cheng, Qian

doi:10.1109/ius46767.2020.9251610

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…PASA is widely used in disease detection [22] , [23] , [26] , [27] , [28] , [29] , [30] because of its ability to accurately quantify molecular physicochemical information difference. However, because the human prostate biopsy needle strips are different from other disease models, the tumor region is usually less than half of its total length.…”

Section: Discussionmentioning

confidence: 99%

“…According to the characteristics of different diseases, different spectral parameters are extracted to quantify changes in the chemical components of the lesion. Previous studies used different PA spectral parameters to study vascular diseases [25] and bone health [26] , [27] , [28] as well as to identify cancer [22] , [23] , [29] , [30] , [31] . In our previous study, we conducted PASA on the entire effective signal; this is because the PA spectral parameter slope indicates the proportion of the high- and low-frequency components of the entire effective signal generated by whole biological tissue [23] , [24] , [29] , [30] , [31] .…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies used different PA spectral parameters to study vascular diseases [25] and bone health [26] , [27] , [28] as well as to identify cancer [22] , [23] , [29] , [30] , [31] . In our previous study, we conducted PASA on the entire effective signal; this is because the PA spectral parameter slope indicates the proportion of the high- and low-frequency components of the entire effective signal generated by whole biological tissue [23] , [24] , [29] , [30] , [31] . However, the prostate biopsy core is different from other samples and has a 15 mm biopsy needle strip; in general, only 20–30% (approximately 4 mm) is tumor region, and the rest is normal prostate tissue [6] , [7] , [8] , [9] .…”

Section: Introductionmentioning

confidence: 99%

“…However, the prostate biopsy core is different from other samples and has a 15 mm biopsy needle strip; in general, only 20–30% (approximately 4 mm) is tumor region, and the rest is normal prostate tissue [6] , [7] , [8] , [9] . In such a situation, the slope reflects the average structure size of the tissue, including the normal region; thus, causing inaccurate results [23] , [24] , [29] , [30] , [31] . To remove the influence of the normal region on the slope parameter, the tumor region must be selected first before PASA.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Chen

et al. 2022

Photoacoustics

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liu

Chen

et al. 2022

Photoacoustics

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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.SignificanceCollagen and lipid are important components of tumor microenvironments (TME) and participates in tumor development and invasion. It has been reported that collagen and lipid can be used as a hallmark to diagnosis and differentiate tumors.AimWe aim to introduce photoacoustic spectral analysis (PASA) method that can provide both the content and structure distribution of endogenous chromophores in biological tissues to characterize the tumor-related features for identifying different types of tumors.ApproachEx vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used in this study. The relative lipid and collagen contents in the TME were assessed based on the PASA parameters and compared with histology. Support vector machine (SVM), one of the simplest machine learning tools, was applied for automatic skin cancer type detection.ResultsThe PASA results showed that the lipid and collagen levels of the tumors were significantly lower than those of the normal tissue, and there was a statistical difference between SCC and BCC (p < 0.05), consistent with the histopathological results. The SVM-based categorization achieved diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC).ConclusionsWe verified the potential use of collagen and lipid in the TME as biomarkers of tumor diversity and achieved accurate tumor classification based on the collagen and lipid content using PASA. The proposed method provides a new way to diagnose tumors.

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Photoacoustic Spectrum Analysis for Quick Identification and Grading of Prostate Cancer

Cited by 5 publications

References 23 publications

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

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

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

Myocardial infarct border demarcation by dual-wavelength photoacoustic spectral analysis

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