ReviewiScience2025
Entropy measures for quantifying complexity in digital pathology and spatial omics.
Review in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatial and temporal intratumoral heterogeneity in breast cancer: a systematic and conceptual review of single-cell and spatial omics studies.BMC cancer · 2026Pooled it
- Cross-Domain Benchmarking of Focus Measures for Smear-Microscopy Autofocus Under a Consensus-Audited Reference.Journal of imaging · 2026Article
- Radiomics analysis of 3D spheroids captures irradiation-induced structural and textural changes.Scientific reports · 2026Article
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- AI-based digital pathology and spatial proteomics enable precision oncology: A case report of recurrent melanoma in a young patient.NPJ precision oncology · 2026Article
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Multiscale domain identification for spatial transcriptomics via persistent homology.Cell reports methods · 2026Article
- A hybrid ST-ViT-driven multimodal architecture combining spatiotemporal MRI patterns and radiomic features for enhanced prediction of pCR in neoadjuvant breast cancer therapy.Biology direct · 2026Article
- Integrating the Contrasting Perspectives Between the Constrained Disorder Principle and Deterministic Optical Nanoscopy: Enhancing Information Extraction from Imaging of Complex Systems.Bioengineering (Basel, Switzerland) · 2026Article
- SlideMamba: entropy-based adaptive fusion of GNN and Mamba for enhanced representation learning in digital pathology.Scientific reports · 2026Article
- A patient-aware benchmarking of CNN and transformer architectures for breast cancer histopathology classification.Frontiers in digital health · 2026Article
- Circadian Rhythm Traits Matter More Than Shift Work Demands in the Sleep-Depression-Safety Behavior Pathway Among Shift-Working Nurses.Journal of nursing management · 2026Article
- Hippocampal subregional texture features associated with Alzheimer's disease severity and cognition.Brain communications · 2026Article
- Automated tumor regression grade assessment and survival prediction in esophageal cancer via weakly supervised multiple instance learning.Frontiers in medicine · 2026Article
- Deep learning-based assessment of PD-L1 expression in NSCLC predicts outcome for patients treated with anti-PD-1 immunotherapy.Frontiers in immunology · 2026Article
- QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit.Computational and structural biotechnology journal · 2026Article
- HDAC2-mediated chromatin remodeling drives hepatocellular carcinoma progression: an integrative analysis of computational pathology and multi-transcriptomics.Journal of translational medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Entropy, a cornerstone of information theory, quantifies disorder, heterogeneity, and complexity in biological systems. This review explores its use in digital pathology and spatial omics, focusing on how entropy captures tissue architecture, spatial heterogeneity, and cellular organization. This review synthesizes entropy's theoretical foundations, spanning classical and spatially-aware metrics, and highlights its applications in mapping tissue heterogeneity, profiling microenvironments, and informing biomarker discovery through AI/ML. Key challenges include computational scalability, interpretability, and the need for standardization. We also discuss future directions, including graph-based entropy for cell networks, dynamic entropy for disease progression, and integrative approaches across molecular and spatial data. By merging theoretical precision with clinical applications, entropy-based methods offer promising tools for advancing precision medicine and personalized treatment strategies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.