ReviewPhysics and imaging in radiation oncology2021
Machine learning applications in radiation oncology.
Review in Physics and imaging in radiation oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 78 citations in OpenAlex.
- Forecasting knee arthroplasty surgery with deep learning : an integrated approach using routine clinical data and radiographs.Bone & joint open · 2026Article
- Evaluation of the reliability of markerless tumor tracking with single-energy and dual-energy imaging using machine learning.Journal of applied clinical medical physics · 2026Article
- Machine learning approach to estimation of in vivo measured dose and treatment planning for total body irradiation.Radiation oncology (London, England) · 2026Article
- Review
- Current status and future directions of multimodal deep learning normal tissue complication probability modelling in head and neck cancer radiotherapy.Frontiers in oncology · 2026Review
- A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer.Physics and imaging in radiation oncology · 2026Article
- Signal-aware deep learning-based respiratory motion prediction for lung tumor management.Frontiers in oncology · 2026Article
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- Decision support using machine learning for predicting adequate bladder filling in prostate radiotherapy: a feasibility study.Radiological physics and technology · 2025Article
- AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis.Discover oncology · 2025Article
- Radiotherapy dosage: A neural network approach for uninvolved liver dose in stereotactic body radiation therapy for liver cancer.World journal of gastrointestinal oncology · 2025Article
- Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Vocal Cord Dysfunction in Nonlaryngeal Head and Neck Cancer After Chemoradiation Therapy: Predictive Modeling Using CT Radiomics and Machine Learning.BioMed research international · 2025Article
- Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready.Journal of medical radiation sciences · 2024Article
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- A comprehensive review of dose limits, triage systems and measurement tools for consequence management of nuclear and radiological emergencies.Radiation physics and chemistry (Oxford, England : 1993) · 2024Article
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review.Clinical Medicine Insights. Oncology · 2024Review
- Assistance systems for patient positioning in radiotherapy practice.Health systems (Basingstoke, England) · 2024Article
- A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer.Physics and imaging in radiation oncology · 2023Article
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.
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.