ReviewMedical physics2020
Genomics models in radiotherapy: From mechanistic to machine learning.
Review in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.Annals of medicine · 2026Article
- Immune and inflammatory radiogenomics of radiotherapy-induced normal-tissue toxicity: genetic susceptibility, mechanisms, risk prediction, and clinical translation.Frontiers in immunology · 2026Review
- From Semantic Modeling to Precision Radiotherapy: An AI Framework Linking Radiobiology, Oncology, and Public Health Integration.Biomedicines · 2025Review
- Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials.Seminars in radiation oncology · 2023Review
- Article
- Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges.Journal of medical imaging (Bellingham, Wash.) · 2021Review
- Biological Pathways Associated With the Development of Pulmonary Toxicities in Mesothelioma Patients Treated With Radical Hemithoracic Radiation Therapy: A Preliminary Study.Frontiers in oncology · 2021Article
- Personalized radioiodine therapy for thyroid cancer patients with known disease.Faculty reviews · 2021Review
- Harnessing genome-wide association studies to minimize adverse radiation-induced side effects.Radiation oncology journal · 2020Article
- Provider Engagement in Radiation Oncology Data Science: Workshop Report.JCO clinical cancer informatics · 2020Article
- National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation.Practical radiation oncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.
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.