Parotid gland fat related Magnetic Resonance image biomarkers improve prediction of late radiation-induced xerostomia

Publication date: Available online 26 June 2018
Source:Radiotherapy and Oncology
Author(s): Lisanne V. van Dijk, Maria Thor, Roel J.H.M. Steenbakkers, Aditya Apte, Tian-Tian Zhai, Ronald Borra, Walter Noordzij, Cherry Estilo, Nancy Lee, Johannes A. Langendijk, Joseph O. Deasy, Nanna M. Sijtsema
PurposeThis study investigated whether Magnetic Resonance image biomarkers (MR-IBMs) were associated with xerostomia 12 months after radiotherapy (Xer12m) and to test the hypothesis that the ratio of fat-to-functional parotid tissue is related to Xer12m. Additionally, improvement of the reference Xer12m model based on parotid gland dose and baseline xerostomia, with MR-IBMs was explored.MethodsParotid gland MR-IBMs of 68 head and neck cancer patients were extracted from pre-treatment T1-weighted MR images, which were normalized to fat tissue, quantifying 21 intensity and 43 texture image characteristics. The performance of the resulting multivariable logistic regression models after bootstrapped forward selection was compared with that of the logistic regression reference model. Validity was tested in a small external cohort of 25 head and neck cancer patients.ResultsHigh intensity MR-IBM P90 (the 90th intensity percentile) values were significantly associated with a higher risk of Xer12m. High P90 values were related to high fat concentration in the parotid glands. The MR-IBM P90 significantly improved model performance in predicting Xer12m (likelihood-ratio-test; p = 0.002), with an increase in internally validated AUC from 0.78 (reference model) to 0.83 (P90). The MR-IBM P90 model also outperformed the reference model (AUC = 0.65) on the external validation cohort (AUC = 0.83).ConclusionPre-treatment MR-IBMs were associated to radiation-induced xerostomia, which supported the hypothesis that the amount of predisposed fat within the parotid glands is associated with Xer12m. In addition, xerostomia prediction was improved with MR-IBMs compared to the reference model.



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