ArticlePhotoacoustics2021
Prostate cancer identification via photoacoustic spectroscopy and machine learning.
Article in Photoacoustics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 37 citations in OpenAlex.
- Optical and Optoacoustic Imaging.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Unsupervised spectra information extraction using physics-informed neural networks in the presence of non-linearities and multi-agent problems.Scientific reports · 2025Article
- Advancements in molecular imaging probes for precision diagnosis and treatment of prostate cancer.Journal of Zhejiang University. Science. B · 2025Review
- Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models.Journal of biophotonics · 2025Article
- Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification.Frontiers in oncology · 2025Article
- Assessing Breast Cancer through Tumor Microenvironment Mapping of Collagen and Other Biomolecule Spectral Fingerprints─A Review.ACS sensors · 2024Review
- TowardsFundamental research · 2024Review
- Identification and Localization of Indolent and Aggressive Prostate Cancers Using Multilevel Bi-LSTM.Journal of imaging informatics in medicine · 2024Article
- Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors.Journal of biomedical optics · 2023Article
- Photoacoustic imaging with limited sampling: a review of machine learning approaches.Biomedical optics express · 2023Review
- Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level.Photoacoustics · 2023Article
- Biomedical Application of Photoacoustics: A Plethora of Opportunities.Micromachines · 2022Review
- Two-step proximal gradient descent algorithm for photoacoustic signal unmixing.Photoacoustics · 2022Article
- Myocardial infarct border demarcation by dual-wavelength photoacoustic spectral analysis.Photoacoustics · 2022Article
- Photoacoustic characterization of bone physico-chemical information.Biomedical optics express · 2022Article
- Quick identification of prostate cancer by wavelet transform-based photoacoustic power spectrum analysis.Photoacoustics · 2022Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoacoustic spectroscopy can generate abundant chemical and physical information about biological tissues. However, this abundance of information makes it difficult to compare these tissues directly. Data mining methods can circumvent this problem. We describe the application of machine-learning methods (including unsupervised hierarchical clustering and supervised classification) to the diagnosis of prostate cancer by photoacoustic spectrum analysis. We focus on the content and distribution of hemoglobin, collagen, and lipids, because these molecules change during the development of prostate cancer. A higher correlation among the ultrasonic power spectra of these chemical components is observed in cancerous than in normal tissues, indicating that the microstructural distributions in cancerous tissues are more consistent. Different classifiers applied in cancer-tissue diagnoses achieved an accuracy of 82 % (better than that of standard clinical methods). The technique thus exhibits great potential for painless early diagnosis of aggressive prostate cancer.
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