ArticleFrontiers in oncology2018
Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.
Article in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.Frontiers in oncology · 2021Pooled it
- Signal-aware deep learning-based respiratory motion prediction for lung tumor management.Frontiers in oncology · 2026Article
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- Review
- Artificial intelligence in automatic image segmentation system for exploring recurrence patterns in small cell carcinoma of the lung.Frontiers in oncology · 2025Article
- Current status and upcoming developments for online adaptive proton therapy enabling a closed feedback loop for near real-time adaptation.Frontiers in oncology · 2025Review
- Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning.Frontiers in artificial intelligence · 2025Article
- A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023.Radiation oncology (London, England) · 2024Article
- The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.Current oncology (Toronto, Ont.) · 2024Review
- Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow.European journal of cancer (Oxford, England : 1990) · 2024Review
- Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review.Clinical Medicine Insights. Oncology · 2024Review
- Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning.Physics and imaging in radiation oncology · 2023Article
- Translation of AI into oncology clinical practice.Oncogene · 2023Review
- Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning.Journal of applied clinical medical physics · 2023Article
- Correlation between patient-specific quality assurance in volumetric modulated arc therapy and 2D dose image features.Scientific reports · 2023Article
- The influence of artificial intelligence on the work of the medical physicist in radiotherapy practice: a short review.BJR open · 2023Review
- Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study.Frontiers in oncology · 2023Article
- Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study.Frontiers in oncology · 2023Article
- A digital physician peer to automatically detect erroneous prescriptions in radiotherapy.NPJ digital medicine · 2022Article
- The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.Journal of applied clinical medical physics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.
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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.