Evidence map›Paper›PMID 36935854›Full record

ArticleClinical and translational radiation oncology2023

Artificial intelligence to predict outcomes of head and neck radiotherapy.

Chulmin Bang, Galaad Bernard, William T Le, Arthur Lalonde, Samuel Kadoury, Houda Bahig

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and translational radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
8.0field-weighted citation impact, top 2% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.

  1. Pooled it
  2. Machine learning for predicting muscle loss after radiotherapy using clinical and toxicity data in oral cavity cancer.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
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  16. Study of late toxicity biomarkers of locally advanced head and neck cancer patients treated with radiotherapy plus cisplatin or cetuximab points to the relevance of skin macrophages (TOX-TTCC-2015-01).Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024
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  20. Early-stage hypopharyngeal squamous cell carcinoma treated with radical radiotherapy at a uniform dose of 70 Gy in 35 fractions: a single-center study.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Chulmin BangCentre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Galaad BernardCentre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
William T LeCentre de recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Arthur LalondeCentre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Samuel KadouryCentre de recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Houda BahigCentre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Centre Hospitalier de l’Université de Montréal · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

Indexed as

ADASYN, adaptive synthetic samplingAI, artificial intelligenceANN, artificial neural networkArtificial intelligenceAUC, Area Under the ROC CurveBMI, body mass indexCancer outcomesCART, Classification and Regression TreeCBCT, cone-beam computed tomographyCIFE, conditional informax feature extractionC-Index, concordance indexCNN, convolutional neural networkCRT, chemoradiationCT, computed tomographyDL, deep learningDM, distant metastasisDSC, Dice Similarity CoefficientDSS, clinical decision support systemsDT, Decision TreeDVH, Dose-volume histogramGANs, Generative Adversarial NetworksGB, Gradient boostingGPU, graphical process unitsHead and neck cancerHNC, head and neck cancerHPV, human papillomavirusHR, hazard ratioIAMB, incremental association Markov blanketIBDM, image based data miningIBMs, image biomarkersIMRT, intensity-modulated RTKNN, k nearest neighborLLR, Local linear forestLR, logistic regressionLRR, loco-regional recurrenceMachine learningMIFS, mutual information based feature selectionML, machine learningMRI, Magnetic resonance imagingMRMR, Minimum redundancy feature selectionN-MLTR, Neural Multi-Task Logistic RegressionNPC, nasopharynxNTCP, Normal Tissue Complication ProbabilityOPC, oropharyngeal cancerORN, osteoradionecrosisOS, overall survivalPCA, Principal component analysisPET, Positron emission tomographyPG, parotid glandsPLR, Positive likelihood ratioPM, pharyngeal mucosaPredictive modelingPreSANet, deep preprocessor module and self-attentionPTV, Planning target volumesQUANTEC, Quantitative Analyses of Normal Tissue Effects in the ClinicRadiomicRFC, random forest classifierRF, random forestRFS, recurrence free survivalRLR, Rigid logistic regressionRRF, Regularized random forestRSF, random survival forestRTLI, radiation-induced temporal lobe injuryRT, radiotherapySDM, shared decision makingSMG, submandibular glandsSMOTE, synthetic minority over-sampling techniqueSTIC, sticky salivaSVC, support vector classifierSVM, support vector machineXGBoost, extreme gradient boosting

Identifiers

PMID36935854
PMCPMC10014342
OpenAlexW4320016403

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.