ArticleJournal of oncology2022
Artificial Intelligence Meets Whole Slide Images: Deep Learning Model Shapes an Immune-Hot Tumor and Guides Precision Therapy in Bladder Cancer.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Corr-A-Net: Interpretable Attention-Based Correlated Feature Learning framework for predicting of HER2 Score in Breast Cancer from H&E Images.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology.Frontiers in oncology · 2024Review
- Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning.NPJ precision oncology · 2023Article
- Molecular classification of urothelial bladder carcinoma.Molecular biology reports · 2023Review
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To construct and validate a deep learning cluster from whole slide images (WSI) for depicting the immunophenotypes and functional heterogeneity of the tumor microenvironment (TME) in patients with bladder cancer (BLCA) and to explore an artificial intelligence (AI) score to explore the underlying biological pathways in the developed WSI cluster. Methods: In this study, the WSI cluster was constructed based on a deep learning procedure. Further rerecognition of TME features in pathological images was applied based on a neural network. Then, we integrated the TCGA cohort and several external testing cohorts to explore and validate this novel WSI cluster and a corresponding quantitative indicator, the AI score. Finally, correlations between the AI cluster (AI score) and classical BLCA molecular subtypes, immunophenotypes, functional heterogeneity, and potential therapeutic method in BLCA were assessed. Results: The WSI cluster was identified associated with clinical survival ( Conclusions: In general, we showed that deep learning can predict prognosis and may aid in the precision medicine for BLCA directly from H&E histology, which is more economical and efficient.
Identifiers
What OpenQuestion 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 OpenQuestion graph.