ArticleCancer research2022
Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.
Article in Cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
63 citing papers in PubMed, 98 citations in OpenAlex.
- An X-ray micro-CT protocol enables non-destructive 3D visualization of glandular architecture in archived paraffin-embedded prostate cancer tissue.The journal of pathology. Clinical research · 2026Article
- Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments.Nature biomedical engineering · 2026Article
- Assessing the Effects of a 3-Dimensional (3D) Pathology Tissue-Processing Workflow on Downstream Molecular Analyses.Laboratory investigation; a journal of technical methods and pathology · 2026Article
- Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026Article
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026Article
- CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.Nature protocols · 2026Review
- Label-free 3D virtual histology of human formalin-fixed paraffin-embedded (FFPE) prostate needle biopsies with propagation-based phase-contrast micro-CT (PBCT).bioRxiv : the preprint server for biology · 2026Article
- Three-dimensional reconstruction of gigapixel whole-mount histopathology specimens with RAPID.Scientific reports · 2026Article
- Assessing the effects of a 3D pathology tissue-processing workflow on downstream molecular analyses.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Biomechanics-Driven 3D Architecture Inference from Histology Using CellSqueeze3D.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- PANDA-PLUS: Improved dataset of prostate whole slide images from PANDA Challenge with pixel-level expert annotations.Journal of pathology informatics · 2026Article
- Contrastive virtual staining enhances deep learning-based PDAC subtyping from H&E-stained tissue cores.The Journal of pathology · 2026Article
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
- A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.Nature biomedical engineering · 2025Article
- First application of three-dimensional light sheet fluorescence microscopy to human testicular tumors: New perspectives in histopathology.Andrology · 2025Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images.PLoS computational biology · 2025Article
- Attention-enhanced hybrid U-Net for prostate cancer grading and explainability.Scientific reports · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
23 authors at 4 institutions in 2 countries.
Funding
Abstract
Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300
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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.