ReviewThe British journal of radiology2019
Applications and limitations of machine learning in radiation oncology.
Review in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses 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
67 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pharmacogenomic scores in psychiatry: systematic review of current evidence.Translational psychiatry · 2024Pooled 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
- From Patient Selection to Surveillance: Artificial Intelligence Applications in Radioiodine Therapy - A Systematic Review.Nuclear medicine and molecular imaging · 2026Review
- Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.Biochemical genetics · 2026Article
- Version upgrades in treatment planning Systems affect clinical application of a multi-Centre knowledge-based planning model.Technical innovations & patient support in radiation oncology · 2026Article
- Prospective blinded clinical evaluation of a fully automated workflow for prostate radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Evaluation of deliverable dose-mimicking automated volumetric arc radiation therapy planning for stage III non-small cell lung cancer patients: comparison with a commercial DVH-predicted automated planning system.Journal of radiation research · 2026Article
- Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark-Based Machine Learning.Scientifica · 2026Article
- An open-source tool for converting 3D mesh volumes into synthetic DICOM CT images for medical physics research.Physical and engineering sciences in medicine · 2025Article
- A Review on Biomarker-Enhanced Machine Learning for Early Diagnosis and Outcome Prediction in Ovarian Cancer Management.Cancer medicine · 2025Review
- AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.Scientific reports · 2025Article
- Time series analysis of dosimetric changes in target volumes and organs at risk monitored by cone beam computed tomography during radiotherapy for non-small-cell lung cancer.Physics and imaging in radiation oncology · 2025Article
- Improving patient specific quality assurance for image registration: clinical use case of target contouring for PET deformable image registration.Physical and engineering sciences in medicine · 2025Article
- Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review.Radiation oncology (London, England) · 2025Review
- Uncertainty-guided pancreatic tumor auto-segmentation with Tversky ensemble.Physics and imaging in radiation oncology · 2025Article
- Radiation therapists' perspectives on artificial intelligence: Insights from a single institution on Improving effectiveness and educational supports.Technical innovations & patient support in radiation oncology · 2025Article
- Targeted radioligand therapy: physics and biology, internal dosimetry and other practical aspects duringTheranostics · 2025Review
- Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs.Clinical and translational radiation oncology · 2024Review
- Article
- Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning interest in machine learning comes the significant risk of misaligned expectations as to what it can and cannot accomplish. This paper evaluates the role of machine learning and the problems it solves within the context of current clinical challenges in radiation oncology. The role of learning algorithms within the workflow for external beam radiation therapy are surveyed, considering simulation imaging, multimodal fusion, image segmentation, treatment planning, quality assurance, and treatment delivery and adaptation. For each aspect, the clinical challenges faced, the learning algorithms proposed, and the successes and limitations of various approaches are analyzed. It is observed that machine learning has largely thrived on reproducibly mimicking conventional human-driven solutions with more efficiency and consistency. On the other hand, since algorithms are generally trained using expert opinion as ground truth, machine learning is of limited utility where problems or ground truths are not well-defined, or if suitable measures of correctness are not available. As a result, machines may excel at replicating, automating and standardizing human behaviour on manual chores, meanwhile the conceptual clinical challenges relating to definition, evaluation, and judgement remain in the realm of human intelligence and insight.
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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.