ArticleInternational journal of radiation oncology, biology, physics2018
Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation.
Article in International journal of radiation oncology, biology, physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
20 citing papers in PubMed.
- Linear Federated Learning for Outcome Prediction With Application to Hepatocellular Carcinoma Radiotherapy.JCO clinical cancer informatics · 2025Article
- Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review.Radiation oncology (London, England) · 2025Review
- Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025Review
- Molecular Profile of Important Genes for Radiogenomics in the Amazon Indigenous Population.Journal of personalized medicine · 2024Article
- Improved outcome models with denoising diffusion.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2024Article
- The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation.Seminars in radiation oncology · 2023Review
- A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).Scientific reports · 2023Article
- Current status and future developments in predicting outcomes in radiation oncology.The British journal of radiology · 2022Review
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models.Journal of biotechnology and biomedicine · 2022Article
- Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.International journal of molecular sciences · 2021Review
- Outcome-based multiobjective optimization of lymphoma radiation therapy plans.The British journal of radiology · 2021Review
- Quantitative PET in the 2020s: a roadmap.Physics in medicine and biology · 2021Article
- Lessons learned in transitioning to AI in the medical imaging of COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2021Review
- Glioma consensus contouring recommendations from a MR-Linac International Consortium Research Group and evaluation of a CT-MRI and MRI-only workflow.Journal of neuro-oncology · 2020Article
- Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy.Journal of the National Cancer Institute · 2020Article
- Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art.Frontiers in oncology · 2020Review
- Three discipline collaborative radiation therapy (3DCRT) special debate: The single most important factor in determining the future of SBRT is immune response.Journal of applied clinical medical physics · 2019Article
- Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.Scientific reports · 2019Article
- Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling.BJR open · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
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.