ArticleScience advances2025
Rapid cancer diagnosis using deep learning-powered label-free subcellular-resolution photoacoustic histology.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Label-free ultraviolet photoacoustic microscopy for multiscale structural phenotyping of epithelial morphogenesis in organoid-derived intestinal epithelial wrinkle tissue.Photoacoustics · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Ultraviolet Sensing-Guided Biomedical Systems: From Label-Free Imaging to Dosimetry and Therapy Feedback.Biosensors · 2026Review
- Photoacoustic Microscopy for Multiscale Biological System Visualization and Clinical Translation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Traditional hematoxylin and eosin staining in formalin-fixed paraffin-embedded sections, while essential for diagnostic pathology, is time-consuming, labor intensive, and prone to artifacts that can obscure critical histological details. Label-free ultraviolet photoacoustic microscopy (UV-PAM) has emerged as a promising alternative, offering fast histology-like images without the need for traditional staining and excessive tissue preparation. However, current UV-PAM systems face challenges in achieving the high spatial resolution required for detailed histological analysis and diagnosis. To address this, we developed a subcellular-resolution UV-PAM (SRUV-PAM) system with a 240-nanometer resolution, enabled by the integration of a high numerical aperture (NA) objective lens (NA = 0.64) and the precise piezo actuators for fine scanning control. This configuration allows visualization of detailed nuclear structures. In addition, we demonstrated virtual staining of SRUV-PAM images via cycle-consistent generative adversarial networks and diagnosis of malignant and benign tumors in liver tissues via densely connected convolutional networks DenseNet-121, achieving an area under the receiver operating characteristic curve of 0.902.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.