ArticleJournal of thoracic disease2023
Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR
Article in Journal of thoracic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical Efficacy and Security Analysis of CDK4/6 Inhibitors Combined with ET in Patients with HRCancer management and research · 2026Article
- Prediction of intraductal cancer microinfiltration based on the hierarchical fusion of peri-tumor imaging histology and dual view deep learning.BMC cancer · 2025Article
- Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology.NPJ precision oncology · 2025Article
- Applying integrated transcriptome and single-cell sequencing analysis to develop a prognostic signature based on M2-like tumor-associated macrophages for breast cancer.Discover oncology · 2025Article
- Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.NPJ precision oncology · 2024Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.Research square · 2024Article
- Innovations in Artificial Intelligence-Driven Breast Cancer Survival Prediction: A Narrative Review.Cancer informatics · 2024Article
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Breast cancer has the highest incidence and mortality rates among women worldwide. Hormone receptor (HR) Methods: This study retrospectively collected hematoxylin and eosin (H&E)-stained slides of HR Results: A total of 421 HR Conclusions: Using a deep-learning-based workflow, we developed models to predict the clinicopathological features, multi-omics features and prognosis of patients with HR
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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.