ArticleNature communications2024
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 59 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
59 citing papers in PubMed, 91 citations in OpenAlex.
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- ALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answering.Nature communications · 2026Article
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Mid-infrared wearable biosensing for metabolic syndrome: Opportunities and translational challenges.iScience · 2026Review
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nature biomedical engineering · 2026Article
- The Role that Biobanks Can Play in Driving Animal-Free Biomedical Research.Expert reviews in molecular medicine · 2026Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Deep learning-based mismatch repair prediction using colorectal cancer macroscopic images: a diagnostic study.Journal of gastroenterology · 2026Article
- Clinical application of PARP inhibitors and emerging strategies to overcome resistance: a pan-cancer perspective.Biomarker research · 2026Review
- Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.NPJ breast cancer · 2026Article
- Single‑center weakly supervised deep learning prediction of KRAS, NRAS, BRAF, and HER2 status in colorectal cancer from histopathology images using internal cross‑validation.Cancer cell international · 2026Article
- HRD-Informed Digital Histology Model for Predicting Platinum Chemo-Response and Prognosis in High-Grade Serous Ovarian Cancer.Interdisciplinary sciences, computational life sciences · 2026Article
- Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.NPJ digital medicine · 2026Article
- Molecular effects of digital psychological intervention for perinatal stress: cell culture, animal model validation, and machine learning-based biomarker identification.Scientific reports · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
17 authors at 9 institutions in 5 countries.
Funding
Abstract
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
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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.