ArticleFrontiers in oncology2021
Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Review
- Three-dimensional spheroid models in breast cancer: tumor microenvironment complexity, cancer stem cell-driven resistance, and translational model integration.Journal of translational medicine · 2026Review
- Patient-derived organoids in gynaecological cancers: emerging tools for therapy and disease modelling.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Physics-informed deep learning enables reliable and scalable organoid quantification for drug screening via OCT.NPJ digital medicine · 2026Article
- Tumor organoid platform design for drug response modeling: culture architecture, microenvironmental complexity, and AI-assisted readouts.Archives of pharmacal research · 2026Review
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Organoids in cancer therapy: translational applications and clinical promise.Molecular cancer · 2026Review
- Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction.Molecular diversity · 2026Article
- The application of experimental models for the drug discovery for digestive tumors.Molecular cancer · 2026Review
- Fast learning-free organoid quantification and tracking with OrganoSeg2.Scientific reports · 2026Article
- Patient-derived organoids in functional precision oncology: from experimental models to clinical decision-making.Frontiers in endocrinology · 2026Review
- Deep Tumor Penetration Using Nanoparticle Delivery Systems: Programmed Design Strategies and Emerging Evaluation Platforms.International journal of nanomedicine · 2026Review
- Organoids and AI-integrated models in ovarian cancer research: the future of personalized therapy.Frontiers in oncology · 2026Review
- Dissecting endometrial cancer complexity in response to standard and targeted therapies.Cell death & disease · 2025Article
- A microfluidic platform for culturing and high-content imaging of adult stem cell-derived organoids.Scientific reports · 2025Article
- Kidney Organoids: Current Advances and Applications.Life (Basel, Switzerland) · 2025Review
- Impact of mouse tracheal basal cell expansion medium on formation and metabolic characteristics of tracheospheres.BMC molecular and cell biology · 2025Article
- A Practical Guide to Developing and Troubleshooting Patient-Derived "Mini-Gut" Colorectal Organoids for Clinical Research.Methods and protocols · 2025Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-quarters of compounds that enter clinical trials fail to make it to market due to safety or efficacy concerns. This statistic strongly suggests a need for better screening methods that result in improved translatability of compounds during the preclinical testing period. Patient-derived organoids have been touted as a promising 3D preclinical model system to impact the drug discovery pipeline, particularly in oncology. However, assessing drug efficacy in such models poses its own set of challenges, and traditional cell viability readouts fail to leverage some of the advantages that the organoid systems provide. Consequently, phenotypically evaluating complex 3D cell culture models remains difficult due to intra- and inter-patient organoid size differences, cellular heterogeneities, and temporal response dynamics. Here, we present an image-based high-content assay that provides object level information on 3D patient-derived tumor organoids without the need for vital dyes. Leveraging computer vision, we segment and define organoids as independent regions of interest and obtain morphometric and textural information per organoid. By acquiring brightfield images at different timepoints in a robust, non-destructive manner, we can track the dynamic response of individual organoids to various drugs. Furthermore, to simplify the analysis of the resulting large, complex data files, we developed a web-based data visualization tool, the Organoizer, that is available for public use. Our work demonstrates the feasibility and utility of using imaging, computer vision and machine learning to determine the vital status of individual patient-derived organoids without relying upon vital dyes, thus taking advantage of the characteristics offered by this preclinical model system.
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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.