ReviewNature biomedical engineering2021
Harnessing non-destructive 3D pathology.
Review in Nature biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 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
83 citing papers in PubMed.
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- 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
- 3D pathology-guided microdissection.Nature methods · 2026Article
- 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
- Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Resilience of the bone marrow vascular architecture to aging and hematological stress.Blood vessels, thrombosis & hemostasis · 2026Article
- Three-dimensional reconstruction of gigapixel whole-mount histopathology specimens with RAPID.Scientific reports · 2026Article
- 3D reconstruction of human liver tissue at cellular resolution.Science advances · 2026Article
- Assessing the effects of a 3D pathology tissue-processing workflow on downstream molecular analyses.bioRxiv : the preprint server for biology · 2026Article
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- Reproducibility of digital pathology features extracted from deep learning and foundational AI models on sequential tissue slides.Scientific reports · 2025Article
- Illuminating Satellite Cells: Light Sheet Fluorescence Microscopy for 3D Imaging of Murine Skeletal Muscles Damaged by Ex Vivo Forced Eccentric Contraction.Microscopy research and technique · 2025Article
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- 3D pathology-guided microdissection.bioRxiv : the preprint server for biology · 2025Article
- Nanobody Immunolabelling and three-dimensional imaging reveals spatially restricted LYVE1 expression by kidney lymphatic vessels in mice.Journal of nanobiotechnology · 2025Article
- Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems.Scientific reports · 2025Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.
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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.