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Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy

Advances in Radiation Oncology(2022)10.1016/j.adro.2022.101163Source: DataRank Database

Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy is a research paper published in Advances in Radiation Oncology (2022). On theSindex it has a DataRank of 0. It has been cited 11 times.

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Open Access11 citations

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Enrichment:Funding (18 grants)Impact (FWCI 1.37)OA: goldTopics (35)IDs (PubMed, PMC, OpenAlex)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (7)

Lisanne V. van Dijk ORCID,Brian Anderson ORCID,Abdallah Mohamed ORCID,Clifton D. Fuller ORCID,Stephen Y. Lai ORCID

Live enrichment

PubMed:36798732 PMC:PMC9926206 OpenAlex:https://openalex.org/W4313256530

National Institute of Dental and Craniofacial Research

Grant: R01DE028290

National Institute of Dental and Craniofacial Research

Grant: R01 DE025248

National Cancer Institute

Grant: R01CA257814

National Cancer Institute

Grant: P30CA016672

KWF Kankerbestrijding

Grant: KWF-13529

KWF Kankerbestrijding

Grant: NWO-452182317

KWF Kankerbestrijding

Grant: NWO-09150162010173

National Science Foundation

Grant: T32CA261856

National Institutes of Health

Grant: 3R01DE028290-02S1

Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac device

National Institutes of Health

Grant: 3P30CA016672-41S4

Cancer Center Support (CORE) Grant

National Institutes of Health

Grant: 5T32CA261856-02

Image Guided Cancer Therapy Training Program

National Institutes of Health

Grant: 5R01CA257814-02

SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck Cancer

National Institutes of Health

Grant: 5R01DE025248-04

Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Establish Objective Clinical Outcome Measures for Mandibular Osteoradionecrosis

Netherlands Organisation for Scientific Research (NWO)

Grant: 13529

Smart Antennas for Astronomy Instrumenation

National Institute of Biomedical Imaging and Bioengineering

Society of Interventional Radiology

The University of Texas MD Anderson Cancer Center

National Institutes of Health

FWCI

1.37

Citation Percentile

0.8%

Citation Trend

2023

2024

2025

2026

goldLicense: cc-by-nc-nd

journalhttp://www.advancesradonc.org/article/S245210942200269X/pdf

publisherhttp://www.advancesradonc.org/article/S245210942200269X/pdf

publisherhttps://api.elsevier.com/content/article/PII:S245210942200269X?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S245210942200269X?httpAccept=text/plain

journalhttps://doi.org/10.1016/j.adro.2022.101163

Fields of Study

Oral health in cancer treatmentHead and Neck Cancer StudiesDental Radiography and Imaging03 medical and health sciences0302 clinical medicine

Keywords

MedicineOsteoradionecrosisAutoencoderLogistic regressionHead and neck cancerRandom forestRadiation therapyArtificial intelligenceMachine learningBinomial regressionDeep learningCohortSurgeryInternal medicineComputer scienceMedical SciencesRadiation MedicineBioinformaticsR895-920610Neoplasms. Tumors. Oncology. Including cancer and carcinogens600Biomedical InformaticsMedical physics. Medical radiology. Nuclear medicineOncologyMedical SpecialtiesMedicine and Health SciencesScientific ArticleRadiologyRC254-282

Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy

FAIR Checklist

Authors (7)

Funding (18 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)