Journal article
Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy
Radiation oncology (London, England), Vol.18(1), 77
05/08/2023
DOI: 10.1186/s13014-023-02274-9
PMCID: PMC10165827
PMID: 37158946
Abstract
Background
This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict radiation induced hyposalivation from dose-volume histograms of the parotid glands.
Methods
The pre and post-radiotherapy salivary flow rates of 510 head and neck cancer patients were used to fit three predictive models of salivary hypofunction, (1) the Lyman-Kutcher-Burman (LKB) model, (2) a spline-based model, (3) a neural network. A fourth LKB-type model using literature reported parameter values was included for reference. Predictive performance was evaluated using a cut-off dependent AUC analysis.
Results
The neural network model dominated the LKB models demonstrating better predictive performance at every cutoff with AUCs ranging from 0.75 to 0.83 depending on the cutoff selected. The spline-based model nearly dominated the LKB models with the fitted LKB model only performing better at the 0.55 cutoff. The AUCs for the spline model ranged from 0.75 to 0.84 depending on the cutoff chosen. The LKB models had the lowest predictive ability with AUCs ranging from 0.70 to 0.80 (fitted) and 0.67 to 0.77 (literature reported).
Conclusion
Our neural network model showed improved performance over the LKB and alternative machine learning approaches and provided clinically useful predictions of salivary hypofunction without relying on summary measures.
Details
- Title: Subtitle
- Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy
- Creators
- Derek K. Smith - American Dental AssociationHaley ClarkAllan Hovan - BC Cancer, Vancouver, BC CanadaJonn Wu - Department of Radiation Oncology, BC Cancer, Vancouver, BC Canada
- Resource Type
- Journal article
- Publication Details
- Radiation oncology (London, England), Vol.18(1), 77
- DOI
- 10.1186/s13014-023-02274-9
- PMID
- 37158946
- PMCID
- PMC10165827
- NLM abbreviation
- Radiat Oncol
- ISSN
- 1748-717X
- eISSN
- 1748-717X
- Publisher
- BioMed Central
- Grant note
- IRG# 15-169-58 / ; K23DE028010 / ;
- Language
- English
- Date published
- 05/08/2023
- Academic Unit
- Preventive and Community Dentistry; Dental Research
- Record Identifier
- 9984966852602771
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