ArticlePhotoacoustics2023
Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level.
Article in Photoacoustics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multi-Task Translation.Diagnostics (Basel, Switzerland) · 2026Article
- Optical and Optoacoustic Imaging.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- 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
- Assessing Breast Cancer through Tumor Microenvironment Mapping of Collagen and Other Biomolecule Spectral Fingerprints─A Review.ACS sensors · 2024Review
- Use of Photoacoustic Imaging to Study the Effects of Anemia on Placental Oxygen Saturation in Normoxic and Hypoxic Conditions.Reproductive sciences (Thousand Oaks, Calif.) · 2024Article
- Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor ProgressionACS sensors · 2024Article
- Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation.Journal of biomedical optics · 2024Review
- Review of Three-Dimensional Handheld Photoacoustic and Ultrasound Imaging Systems and Their Applications.Sensors (Basel, Switzerland) · 2023Review
- Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors.Journal of biomedical optics · 2023Article
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Authors and funding
7 authors.
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Abstract
Breast cancer threatens the health of women worldwide, and its molecular subtypes largely determine the therapy and prognosis of patients. However, an uncomplicated and accurate method to identify subtypes is currently lacking. This study utilized photoacoustic spectral analysis (PASA) based on the partial least squares discriminant algorithm (PLS-DA) to identify molecular breast cancer subtypes at the biomacromolecular level in vivo. The area of power spectrum density (APSD) was extracted to semi-quantify the biomacromolecule content. The feature wavelengths were obtained via the variable importance in projection (VIP) score and the selectivity ratio (Sratio), to identify the biomarkers. The PASA achieved an accuracy of 84%. Most of the feature wavelengths fell into the collagen-dominated absorption waveband, which was consistent with the histopathological results. This paper proposes a successful method for identifying molecular breast cancer subtypes and proves that collagen can be treated as a biomarker for molecular breast cancer subtyping.
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