ReviewJournal of oncology2021
Investigation of Radiation-Induced Toxicity in Head and Neck Cancer Patients through Radiomics and Machine Learning: A Systematic Review.
Review in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- Prospective evaluation of dose rate effects on acute radiation toxicities and quality of life in head and neck cancer.Journal of the Egyptian National Cancer Institute · 2026Article
- Review
- A Pilot Study of Multimodal Dosiomics and Longitudinal Delta-Radiomics for Predicting Radiation-Induced Xerostomia in Head-and-Neck Cancer.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Review
- CNN-based prediction using early post-radiotherapy MRI as a proxy for toxicity in the murine head and neck.Acta oncologica (Stockholm, Sweden) · 2025Article
- Ensemble learning approach for prediction of early complications after radiotherapy for head and neck cancer using CT and MRI radiomic features.Scientific reports · 2025Article
- Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use.Radiology. Artificial intelligence · 2024Review
- Article
- Genetic profiling in radiotherapy: a comprehensive review.Frontiers in oncology · 2024Review
- Impact of radiation dose on patient-reported acute taste alteration in a prospective observational study cohort in head and neck squamous cell cancer (HNSCC).La Radiologia medica · 2023Observational
- Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.Diagnostics (Basel, Switzerland) · 2023Review
- [BMC medical imaging · 2023Article
- Assessing the generalisability of radiomics features previously identified as predictive of radiation-induced sticky saliva and xerostomia.Physics and imaging in radiation oncology · 2023Article
- Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.Frontiers in oncology · 2022Review
- Article
- Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MATERIALS AND
methodsA systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported.
resultsEight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases.
conclusionsPublished radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities.
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.