ReviewPhysics in medicine and biology2017
Radiogenomics and radiotherapy response modeling.
Review in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed, 59 citations in OpenAlex.
- Personalised Dosimetry in Nuclear Medicine: Bridging Physics, Biology and AI for Next Generation Radiopharmaceutical Therapy.Nuclear medicine and molecular imaging · 2026Review
- Review
- Health economics evaluation of artificial intelligence in the field of oncology: a scoping review.Health economics review · 2026Review
- Advancements in Targeted Radiopharmaceuticals: Innovations in Diagnosis and Therapy for Enhanced Cancer Management.Chembiochem : a European journal of chemical biology · 2026Review
- Voxel-Based Dose-Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective.Journal of clinical medicine · 2025Article
- NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy.International journal of radiation oncology, biology, physics · 2025Review
- Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis.Interactive journal of medical research · 2024Article
- A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).Scientific reports · 2023Article
- Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.Frontiers in oncology · 2023Article
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
- Uniqueness of radiomic features in non-small cell lung cancer.Journal of applied clinical medical physics · 2022Article
- Current status and future developments in predicting outcomes in radiation oncology.The British journal of radiology · 2022Review
- Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.Cancers · 2022Review
- Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.International journal of molecular sciences · 2021Review
- Manganese Ferrite Nanoparticles Enhance the Sensitivity of Hepa1-6 Hepatocellular Carcinoma to Radiation by Remodeling Tumor Microenvironments.International journal of molecular sciences · 2021Article
- Roadmap: proton therapy physics and biology.Physics in medicine and biology · 2021Review
- Article
- Harnessing genome-wide association studies to minimize adverse radiation-induced side effects.Radiation oncology journal · 2020Article
- Radiomics for liver tumours.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Review
- Provider Engagement in Radiation Oncology Data Science: Workshop Report.JCO clinical cancer informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.
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.