ArticleBlood cancer discovery2022
Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine.
Article in Blood cancer discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Proteomic Stability and Ex Vivo Compatibility of a Processed Phospholipoproteic Secretome-Derived Formulation.Pharmaceutics · 2026Article
- Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells.bioRxiv : the preprint server for biology · 2025Article
- Neural Vulnerabilities in Glioblastoma: Rethinking Therapy Through Neuroactive Drug Repurposing.Brain tumor research and treatment · 2025Review
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Article
- Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts.Science advances · 2025Article
- Ex vivo imaging-based high content phenotyping of patients with rheumatoid arthritis.EBioMedicine · 2025Article
- Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia.Nature communications · 2024Article
- Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.Nature communications · 2024Article
- Molecular and functional landscape of malignant serous effusions for precision oncology.Nature communications · 2024Article
- Efficacy and feasibility of pharmacoscopy-guided treatment for acute myeloid leukemia patients who have exhausted all registered therapeutic options.Haematologica · 2024Article
- Robust scoring of selective drug responses for patient-tailored therapy selection.Nature protocols · 2024Review
- Standardized assays to monitor drug sensitivity in hematologic cancers.Cell death discovery · 2023Article
- Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma.Nature cancer · 2023Article
- Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence.Journal of the American Chemical Society · 2023Review
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 2 countries.
Funding
Abstract
Drug testing in patient biopsy-derived cells can identify potent treatments for patients suffering from relapsed or refractory hematologic cancers. Here we investigate the use of weakly supervised deep learning on cell morphologies (DML) to complement diagnostic marker-based identification of malignant and nonmalignant cells in drug testing. Across 390 biopsies from 289 patients with diverse blood cancers, DML-based drug responses show improved reproducibility and clustering of drugs with the same mode of action. DML does so by adapting to batch effects and by autonomously recognizing disease-associated cell morphologies. In a post hoc analysis of 66 patients, DML-recommended treatments led to improved progression-free survival compared with marker-based recommendations and physician's choice-based treatments. Treatments recommended by both immunofluorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identification of effective personalized treatments. SIGNIFICANCE: We have recently demonstrated that image-based drug screening in patient samples identifies effective treatment options for patients with advanced blood cancers. Here we show that using deep learning to identify malignant and nonmalignant cells by morphology improves such screens. The presented workflow is robust, automatable, and compatible with clinical routine. This article is highlighted in the In This Issue feature, p. 476.
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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.