ArticleThe Lancet. Digital health2021
Towards computationally efficient prediction of molecular signatures from routine histology images.
Article in The Lancet. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
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Who cites it
8 citing papers in PubMed.
- Multimodal Fusion of 3D CT and Pathological Images for Gastric Cancer Recurrence Prediction.Cancer management and research · 2026Article
- Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.The Journal of pathology · 2025Article
- Prediction of recurrence risk in endometrial cancer with multimodal deep learning.Nature medicine · 2024Article
- Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy.NPJ precision oncology · 2024Article
- Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.Nature genetics · 2024Article
- Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview.Journal of translational medicine · 2024Review
- Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images.Cancers · 2023Article
- The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers.Computational and structural biotechnology journal · 2022Review
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2 authors.
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Abstract
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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.