ArticleCancers2021
A Patient-Derived Organoid-Based Radiosensitivity Model for the Prediction of Radiation Responses in Patients with Rectal Cancer.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses 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
37 citing papers in PubMed, 3 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Global research trends and hotspots of colorectal cancer organoids: a bibliometric insight and visualization analysis via multiple databases.Frontiers in oncology · 2026Pooled it
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Systematic review: predictive value of organoids in colorectal cancer.Scientific reports · 2023Pooled it
- From bench to bedside: advancing our understanding of radioresistance in rectal cancer.Cancer metastasis reviews · 2026Review
- Patient-Derived Functional Models for Prediction of Radiotherapy Response in Rectal Cancer: A Systematic Review and Exploratory HSROC Meta-Analysis.Life (Basel, Switzerland) · 2026Review
- High-throughput radiation sensitivity screening of 3D-head and neck squamous cell carcinoma (HNSCC) organoids using an automated radiation modulator (ARM).Materials today. Bio · 2026Article
- A translational colorectal cancer organoid biobank mirrors patients' tumor histology, molecular profiles, and treatment responses.Journal of experimental & clinical cancer research : CR · 2026Article
- Tetrabenazine-induced miR-34a-5p suppresses the tumorigenicity of radioresistant colorectal cancer by inhibiting M2 macrophage polarization.Cell communication and signaling : CCS · 2025Article
- Combined MEK and PARP inhibition enhances radiation response in rectal cancer.Cell reports. Medicine · 2025Article
- Review
- Liver quad culture chip as a model for radiation injury research.Scientific reports · 2025Article
- Continuous imaging to evaluate growth and drug responses of patient-derived colorectal tumouroids.ESMO gastrointestinal oncology · 2025Article
- Modeling neurodegenerative diseases with brain organoids: from development to disease applications.Frontiers in cell and developmental biology · 2025Review
- A New Perspective on Precision Medicine: The Power of Digital Organoids.Biomaterials research · 2025Review
- Advancements in Research and Treatment Applications of Patient-Derived Tumor Organoids in Colorectal Cancer.Cancers · 2024Review
- A Decade of Organoid Research: Progress and Challenges in the Field of Organoid Technology.ACS omega · 2024Review
- Patient-derived tumor organoids: a new avenue for preclinical research and precision medicine in oncology.Experimental & molecular medicine · 2024Review
- Immune-related adverse events of antibody-based biological medicines in cancer therapy.Journal of cellular and molecular medicine · 2024Review
- Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models.Medicine in novel technology and devices · 2024Article
- The application of organoids in colorectal diseases.Frontiers in pharmacology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Patient-derived tumor organoids closely resemble original patient tumors. We conducted this co-clinical trial with treatment-naive rectal cancer patients and matched patient-derived tumor organoids to determine whether a correlation exists between experimental results obtained after irradiation in patients and organoids. Between November 2017 and March 2020, we prospectively enrolled 33 patients who were diagnosed with mid-to-lower rectal adenocarcinoma based on endoscopic biopsy findings. We constructed a prediction model through a machine learning algorithm using clinical and experimental radioresponse data. Our data confirmed that patient-derived tumor organoids closely recapitulated original tumors, both pathophysiologically and genetically. Radiation responses in patients were positively correlated with those in patient-derived tumor organoids. Our machine learning-based prediction model showed excellent performance. In the prediction model for good responders trained using the random forest algorithm, the area under the curve, accuracy, and kappa value were 0.918, 81.5%, and 0.51, respectively. In the prediction model for poor responders, the area under the curve, accuracy, and kappa value were 0.971, 92.1%, and 0.75, respectively. Our patient-derived tumor organoid-based radiosensitivity model could lead to more advanced precision medicine for treating patients with rectal cancer.
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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.