Mean parotid dose prediction model using machine learning regression method for intensity-modulated radiotherapy in head and neck cancer

Parotids are considered one of the major organs at risk in Head and Neck (HN) intensity-modulated radiotherapy (IMRT). Achieving proper target coverage with reduced mean parotid dose demands an elaborate time-consuming IMRT plan optimization. A parotid mean dose prediction model based on a machine-l...

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Bibliographic Details
Published in:	Medical dosimetry : official journal of the American Association of Medical Dosimetrists Vol. 46; no. 3; pp. 283 - 288
Main Authors:	Ranjith, CP, puzhakkal, Niyas, Arunkrishnan, MP, Vysakh, R, Irfad, MP, Vijayagopal, KS, Jayashanker, S
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01-01-2021
Subjects:	Head and neck cancer Parotid mean dose Supervised machine learning Head and neck cancer Supervised machine learning Parotid mean dose
Online Access:	Get full text
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Summary:	Parotids are considered one of the major organs at risk in Head and Neck (HN) intensity-modulated radiotherapy (IMRT). Achieving proper target coverage with reduced mean parotid dose demands an elaborate time-consuming IMRT plan optimization. A parotid mean dose prediction model based on a machine-learning linear regression was developed and validated in this study. The model was developed using independent variables, such as parotid to PTV overlapping volume, dose coverage of the overlapping PTV, the ratio of overlapping parotid volume to total parotid volume, and volume of parotid overlapping with isotopically expanded PTV contours. The Pearson correlation coefficients between these independent variables and the mean parotid dose were calculated. Multicollinearity of the independent variables was checked by calculating the Variance Inflation Factor (VIF). All variables are having VIF less than ten were taken for the model. Fifty IMRT patient plans were used to develop the model. The mean parotid dose predicted by the model was in good agreement with the obtained mean parotid dose. The model is having a Root Mean Square Error (RMSE) of 2.89 Gy and an R-square of 0.7695. The model was successfully validated using the fivefold cross-validation method, resulting R-square value of 0.6179 and an RMSE of 2.93 Gy. The normality of the model's residuals was tested using Quartile-Quartile (Q-Q) plot and Shapiro Wilk test (p = 0.996, for null hypothesis ``residuals were normally distributed''). The data points in the Q-Q plot are falling approximately along the reference line. This model can be used in clinics to help the planner in the preplanning phase for efficient plan optimization.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0958-3947 1873-4022
DOI:	10.1016/j.meddos.2021.02.003