ArticleCancers2023
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed, 53 citations in OpenAlex.
- Enhanced breast cancer detection in histopathology images using graph transformer GANs with graph masked modeling and osprey optimization.Journal of molecular histology · 2026Article
- Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning.PLoS computational biology · 2026Article
- DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma.Journal of ovarian research · 2026Article
- HisCMCL: cross-modal contrastive learning with hierarchical multi-scale fusion for spatial expression prediction.Bioinformatics (Oxford, England) · 2026Article
- Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Prediction of molecular subtypes from histology: AI-driven analysis of prostate cancer morphological patterns and therapeutic implications.NPJ precision oncology · 2026Article
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Reproducibility of digital pathology features extracted from deep learning and foundational AI models on sequential tissue slides.Scientific reports · 2025Article
- Influence of preprocessing of stimulated Raman scattering images on the performance of deep neural networks for detecting cancer tissue.Quantitative imaging in medicine and surgery · 2025Article
- Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning.Briefings in bioinformatics · 2025Article
- DANet: spatial gene expression prediction from H&E histology images through dynamic alignment.Briefings in bioinformatics · 2025Article
- Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.Scientific reports · 2025Article
- Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.Scientific reports · 2025Article
- A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.Genes · 2025Review
- Combining spatial transcriptomics with tissue morphology.Nature communications · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialised platforms, and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA sequencing techniques to predict the expression of 138 genes (incorporated from 6 commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E)-stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n = 335). We demonstrate successful gene prediction on a held-out test set (n = 160, corr = 0.82 across patients, corr = 0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n = 498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index = 0.56, hazard ratio = 2.16 (95% CI 1.12-3.06),
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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.