ArticleThe Journal of pathology2023
Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.
Article in The Journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Current Advances in Spatial Pathology of Nasal Polyps: Challenges, Opportunities for Artificial Intelligence, and Future Directions.World journal of otorhinolaryngology - head and neck surgery · 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
- 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
- A Comprehensive Review of Nuclear Mechanics: Advances, Disease Relevance, Methodologies, and AI Applications.Cell biochemistry and biophysics · 2026Review
- Assessing the effects of a 3D pathology tissue-processing workflow on downstream molecular analyses.bioRxiv : the preprint server for biology · 2026Article
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- First application of three-dimensional light sheet fluorescence microscopy to human testicular tumors: New perspectives in histopathology.Andrology · 2025Article
- First Decade of the National Cancer Institute's Affordable Cancer Technologies Program: Accelerating Translational Technology Research and Development for Cancer Globally.JCO global oncology · 2025Review
- Three-dimensional assessments are necessary to determine the true, spatially resolved composition of tissues.Cell reports methods · 2025Article
- A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.Journal of pathology informatics · 2024Article
- Article
- An end-to-end workflow for nondestructive 3D pathology.Nature protocols · 2024Review
- Article
- Direct three-dimensional segmentation of prostate glands with nnU-Net.Journal of biomedical optics · 2024Article
- Engineering the future of 3D pathology.The journal of pathology. Clinical research · 2024Article
- An end-to-end workflow for non-destructive 3D pathology.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.
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