ReviewFrontiers in oncology2020
Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy.
Review in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Article
- Experimental workflow to evaluate nanoparticles combined with photon and carbon-ion medical irradiations in spheroids.Discover nano · 2026Article
- Toward a Conceptual Multiscale Framework for Predictive Radiobiology: Integrating Genomic Damage, Network Rewiring, and Tissue Microenvironment.International journal of molecular sciences · 2026Review
- From Cellular Radiosensitivity to Precision Radiotherapy: Integrating Functional Assays, Genomics, and Clinical Modeling.Cancers · 2026Review
- Spatial Dosimetric-Based Prediction of Long-Term Urinary Toxicity After Permanent Prostate Brachytherapy.Cancers · 2026Article
- Benchmarking of radiobiological NTCP models in head and neck radiotherapy using independent computational pipelines: an institutional validation study with machine learning augmentation.Ecancermedicalscience · 2026Article
- Article
- AI-enabled precision prediction and proactive management of cutaneous toxicities in cancer immunoradiotherapy (ICI+RT).Frontiers in oncology · 2026Review
- Development of a Machine Learning-Based Prognostic Model Using Systemic Inflammation Markers in Patients Receiving Nivolumab Immunotherapy: A Real-World Cohort Study.Journal of personalized medicine · 2025Article
- Machine learning-based prediction of severe oral mucositis from head and neck cancer radiation therapy.Radiation oncology journal · 2025Article
- Identifying the optimal time point for adaptive re-planning in prostate cancer radiotherapy to minimise rectal toxicity using normal tissue imaging biomarkers.Physics and imaging in radiation oncology · 2025Article
- CNN-based prediction using early post-radiotherapy MRI as a proxy for toxicity in the murine head and neck.Acta oncologica (Stockholm, Sweden) · 2025Article
- Review
- Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity.Advances in radiation oncology · 2025Article
- Article
- Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Article
- NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy.International journal of radiation oncology, biology, physics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.
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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.