ArticleCancers2021
Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 28 citations in OpenAlex.
- Morphomechanically-Informed Spatial Curvature Sequencing in Prostate Cancer.bioRxiv : the preprint server for biology · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Combining spatial transcriptomics with tissue morphology.Nature communications · 2025Review
- Tumour hypoxia in driving genomic instability and tumour evolution.Nature reviews. Cancer · 2025Review
- Application of Spatial Transcriptomics in Digestive System Tumors.Biomolecules · 2024Review
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Deep learning in spatially resolved transcriptfomics: a comprehensive technical viewBriefings in bioinformatics · 2024Article
- Digital pathology and multimodal learning on oncology data.BJR artificial intelligence · 2024Review
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Article
- Experimental in vitro, ex vivo and in vivo models in prostate cancer research.Nature reviews. Urology · 2023Review
- SPASCER: spatial transcriptomics annotation at single-cell resolution.Nucleic acids research · 2023Article
- Novel insights from spatial transcriptome analysis in solid tumors.International journal of biological sciences · 2023Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers.Cancers · 2022Article
- Computer vision for plant pathology: A review with examples from cocoa agriculture.Applications in plant sciencesArticle
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
Abstract
Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.
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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.