ArticleCancer research2023
Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network.
Article in Cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed.
- Raman spectroscopic signatures of prostate cancer progression: correlation with gleason score.Molecular and cellular biochemistry · 2026Article
- Gut and Glomerular Barriers Determine Nanoplastic Fate and Systemic Impact.ACS environmental Au · 2026Article
- Imaging strategies for delineating glioma margins: Aiming at precise therapy.Fundamental research · 2026Review
- AI-Assisted Coherent Raman Scattering Microscopy for Clinical Translation.Chemical & biomedical imaging · 2026Review
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Real-Time In Vivo Cellular-Level Imaging During Puncture.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- 4Pi stimulated Raman scattering for label-free super-resolution chemical imaging.Science advances · 2026Article
- DeepLuAd: Semantic-guided virtual histopathology of lung adenocarcinoma via stimulated Raman scattering.Theranostics · 2026Article
- Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy.Scientific reports · 2025Article
- Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects.Journal of pharmaceutical analysis · 2025Review
- 4-Pi Stimulated Raman Scattering for Label-free Super-resolution Chemical Imaging.bioRxiv : the preprint server for biology · 2025Article
- Influence of preprocessing of stimulated Raman scattering images on the performance of deep neural networks for detecting cancer tissue.Quantitative imaging in medicine and surgery · 2025Article
- Self-Calibrated Stimulated Raman Scattering Spectroscopy for Rapid Cholangiocarcinoma Diagnosis.Analytical chemistry · 2025Article
- High-content stimulated Raman pathology imaging and transcriptomics reveal leukemia subtype-specific lipid metabolic heterogeneity.Frontiers in immunology · 2025Article
- Enhanced NIR-II Nanoparticle Probe for PSMA-Targeted Molecular Imaging and Prostate Cancer Diagnosis.International journal of nanomedicine · 2025Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Artificial Intelligence-Assisted Stimulated Raman Histology: New Frontiers in Vibrational Tissue Imaging.Cancers · 2024Review
- Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Advances in nanotechnological approaches for the detection of early markers associated with severe cardiac ailments.Nanomedicine (London, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Focal therapy (FT) has been proposed as an approach to eradicate clinically significant prostate cancer while preserving the normal surrounding tissues to minimize treatment-related toxicity. Rapid histology of core needle biopsies is essential to ensure the precise FT for localized lesions and to determine tumor grades. However, it is difficult to achieve both high accuracy and speed with currently available histopathology methods. Here, we demonstrated that stimulated Raman scattering (SRS) microscopy could reveal the largely heterogeneous histologic features of fresh prostatic biopsy tissues in a label-free and near real-time manner. A diagnostic convolutional neural network (CNN) built based on images from 61 patients could classify Gleason patterns of prostate cancer with an accuracy of 85.7%. An additional 22 independent cases introduced as external test dataset validated the CNN performance with 84.4% accuracy. Gleason scores of core needle biopsies from 21 cases were calculated using the deep learning SRS system and showed a 71% diagnostic consistency with grading from three pathologists. This study demonstrates the potential of a deep learning-assisted SRS platform in evaluating the tumor grade of prostate cancer, which could help simplify the diagnostic workflow and provide timely histopathology compatible with FT treatment. SIGNIFICANCE: A platform combining stimulated Raman scattering microscopy and a convolutional neural network provides rapid histopathology and automated Gleason scoring on fresh prostate core needle biopsies without complex tissue processing.
Indexed as
Identifiers
What OpenQuestion holds
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.