BACKGROUND AND PURPOSE: Current models for the prediction of late patient-rated moderate-to-severe xerostomia (XER12m) and sticky saliva (STIC12m) after radiotherapy are based on dose-volume parameters and baseline xerostomia (XERbase) or sticky saliva (STICbase) scores. The purpose is to improve prediction of XER12m and STIC12m with patient-specific characteristics, based on CT image biomarkers (IBMs). METHODS: Planning CT-scans and patient-rated outcome measures were prospectively collected for 249 head and neck cancer patients treated with definitive radiotherapy with or without systemic treatment. The potential IBMs represent geometric, CT intensity and textural characteristics of the parotid and submandibular glands. Lasso regularisation was used to create multivariable logistic regression models, which were internally validated by bootstrapping. RESULTS: The prediction of XER12m could be improved significantly by adding the IBM "Short Run Emphasis" (SRE), which quantifies heterogeneity of parotid tissue, to a model with mean contra-lateral parotid gland dose and XERbase. For STIC12m, the IBM maximum CT intensity of the submandibular gland was selected in addition to STICbase and mean dose to submandibular glands. CONCLUSION: Prediction of XER12m and STIC12m was improved by including IBMs representing heterogeneity and density of the salivary glands, respectively. These IBMs could guide additional research to the patient-specific response of healthy tissue to radiation dose.
BACKGROUND AND PURPOSE: Current models for the prediction of late patient-rated moderate-to-severe xerostomia (XER12m) and sticky saliva (STIC12m) after radiotherapy are based on dose-volume parameters and baseline xerostomia (XERbase) or sticky saliva (STICbase) scores. The purpose is to improve prediction of XER12m and STIC12m with patient-specific characteristics, based on CT image biomarkers (IBMs). METHODS: Planning CT-scans and patient-rated outcome measures were prospectively collected for 249 head and neck cancerpatients treated with definitive radiotherapy with or without systemic treatment. The potential IBMs represent geometric, CT intensity and textural characteristics of the parotid and submandibular glands. Lasso regularisation was used to create multivariable logistic regression models, which were internally validated by bootstrapping. RESULTS: The prediction of XER12m could be improved significantly by adding the IBM "Short Run Emphasis" (SRE), which quantifies heterogeneity of parotid tissue, to a model with mean contra-lateral parotid gland dose and XERbase. For STIC12m, the IBM maximum CT intensity of the submandibular gland was selected in addition to STICbase and mean dose to submandibular glands. CONCLUSION: Prediction of XER12m and STIC12m was improved by including IBMs representing heterogeneity and density of the salivary glands, respectively. These IBMs could guide additional research to the patient-specific response of healthy tissue to radiation dose.
Authors: Valerio Nardone; Paolo Tini; Christophe Nioche; Maria Antonietta Mazzei; Tommaso Carfagno; Giuseppe Battaglia; Pierpaolo Pastina; Roberta Grassi; Lucio Sebaste; Luigi Pirtoli Journal: Radiol Med Date: 2018-01-24 Impact factor: 3.469
Authors: Benjamin S Rosen; Peter G Hawkins; Daniel F Polan; James M Balter; Kristy K Brock; Justin D Kamp; Christina M Lockhart; Avraham Eisbruch; Michelle L Mierzwa; Randall K Ten Haken; Issam El Naqa Journal: Int J Radiat Oncol Biol Phys Date: 2018-07-10 Impact factor: 7.038
Authors: Joel R Wilkie; Michelle L Mierzwa; Keith A Casper; Charles S Mayo; Matthew J Schipper; Avraham Eisbruch; Francis P Worden; Issam El Naqa; Benjamin L Viglianti; Benjamin S Rosen Journal: Radiother Oncol Date: 2020-04-06 Impact factor: 6.280