ArticlePhotoacoustics2022
Quick identification of prostate cancer by wavelet transform-based photoacoustic power spectrum analysis.
Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Volumetric photoacoustic/ultrasound image fusion and enhancement with dual-tree complex wavelet transform.Biomedical optics express · 2026Article
- A joint photoacoustic imaging and broadband spectral analysis for early-stage intraoperative pathology assessment: A case study with colorectal cancer.Photoacoustics · 2025Article
- Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification.Frontiers in oncology · 2025Article
- Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models.Journal of biophotonics · 2025Article
- Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors.Journal of biomedical optics · 2023Article
- Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level.Photoacoustics · 2023Article
- Improving photoacoustic imaging in low signal-to-noise ratio by using spatial and polarity coherence.Photoacoustics · 2022Article
- Biomedical Application of Photoacoustics: A Plethora of Opportunities.Micromachines · 2022Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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