ArticleNature communications2023
Next-Generation Morphometry for pathomics-data mining in histopathology.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 2 of them syntheses that pooled it.
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Who cites it
87 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-powered renal pathology for glomerular disease classification: a systematic review and meta-analysis.Renal failure · 2026Pooled it
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Incorporating Pathomics Into IgAN Risk Prediction.Kidney international reports · 2026Article
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.Kidney international reports · 2026Review
- Converting whole slide images from DICOM to ScanScope Virtual Slide-like TIFF: a practical workaround for teaching, research, and collaborative diagnosis.Virchows Archiv : an international journal of pathology · 2026Article
- Individualised treatment effects of corticosteroids in IgA nephropathy.EBioMedicine · 2026Article
- The expanding role of biomarkers in the management of IgA nephropathy.Kidney international supplements · 2026Review
- Deep Learning Morphometric Analysis on Protocol Biopsies Predicts Future Graft Function.Kidney international reports · 2026Article
- A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.BMC cancer · 2026Article
- Development and validation of pathomics signature for predicting prognosis of advanced high-grade serous ovarian carcinoma patients after platinum-based chemotherapy.Scientific reports · 2026Article
- A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.NPJ digital medicine · 2026Article
- Streamlining the Histopathologic Workflow in Diabetic Kidney Disease with Artificial Intelligence.Journal of the American Society of Nephrology : JASN · 2026Article
- Graph neural networks for computational nephrology.Nature reviews. Nephrology · 2026Article
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Semantic Segmentation for Pixel-Wise Visualization of Breast Cancer in Deep Ultraviolet-Excited Fluorescence Images.Acta histochemica et cytochemica · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Artificial Intelligence for Diagnostic Pathology in Nephrology.Journal of the American Society of Nephrology : JASN · 2026Article
- Clinical application of deep learning-based measurement of glomerular morphometry in IgAN.Frontiers in immunology · 2026Article
27 more citing papers are in PubMed but not listed here.
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17 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.
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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.