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Radiol Med. 2018 Jun;123(6):415-423. doi: 10.1007/s11547-017-0850-7. Epub 2018 Jan 24.

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT.

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Istituto Toscano Tumori, Florence, Italy.
Unit of Radiation Oncology, University Hospital of Siena, Viale Bracci, 53100, Siena, Italy.
Istituto Toscano Tumori, Florence, Italy.
Unit of Radiation Oncology, University Hospital of Siena, Viale Bracci, 53100, Siena, Italy.
Sbarro Health Research Organization, Temple University, Philadelphia, PA, USA.
IMIV, CEA, Inserm, CNRS, Univ. Paris-Sud, Université Paris Saclay, CEA-SHFJ, 91 400, Orsay, France.
Department of Medical, Surgical and Neuro Sciences, Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy.
Unit of Radiation Oncology, University Hospital of Florence, Florence, Italy.
Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA, USA.



Image texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers.


We performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dose < 26 Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia.


Seventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (p < 0.001, R2:0.664).


Xerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture analysis could enhance the normal dose constraints for the prediction of xerostomia.


Head and neck cancer; Radiation therapy; Texture analysis; Xerostomia

[Indexed for MEDLINE]

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