Lisanne V van Dijk1, Walter Noordzij2, Charlotte L Brouwer3, Ronald Boellaard2, Johannes G M Burgerhof4, Johannes A Langendijk3, Nanna M Sijtsema3, Roel J H M Steenbakkers3. 1. Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. Electronic address: l.v.van.dijk@umcg.nl. 2. Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands. 3. Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. 4. Department of Epidemiology, University of Groningen, University Medical Center Groningen, The Netherlands.
Abstract
BACKGROUND AND PURPOSE: Current prediction of radiation-induced xerostomia 12months after radiotherapy (Xer12m) is based on mean parotid gland dose and baseline xerostomia (Xerbaseline) scores. The hypothesis of this study was that prediction of Xer12m is improved with patient-specific characteristics extracted from 18F-FDG PET images, quantified in PET image biomarkers (PET-IBMs). PATIENTS AND METHODS: Intensity and textural PET-IBMs of the parotid gland were collected from pre-treatment 18F-FDG PET images of 161 head and neck cancer patients. Patient-rated toxicity was prospectively collected. Multivariable logistic regression models resulting from step-wise forward selection and Lasso regularisation were internally validated by bootstrapping. The reference model with parotid gland dose and Xerbaseline was compared with the resulting PET-IBM models. RESULTS: High values of the intensity PET-IBM (90th percentile (P90)) and textural PET-IBM (Long Run High Grey-level Emphasis 3 (LRHG3E)) were significantly associated with lower risk of Xer12m. Both PET-IBMs significantly added in the prediction of Xer12m to the reference model. The AUC increased from 0.73 (0.65-0.81) (reference model) to 0.77 (0.70-0.84) (P90) and 0.77 (0.69-0.84) (LRHG3E). CONCLUSION: Prediction of Xer12m was significantly improved with pre-treatment PET-IBMs, indicating that high metabolic parotid gland activity is associated with lower risk of developing late xerostomia. This study highlights the potential of incorporating patient-specific PET-derived functional characteristics into NTCP model development.
BACKGROUND AND PURPOSE: Current prediction of radiation-induced xerostomia 12months after radiotherapy (Xer12m) is based on mean parotid gland dose and baseline xerostomia (Xerbaseline) scores. The hypothesis of this study was that prediction of Xer12m is improved with patient-specific characteristics extracted from 18F-FDG PET images, quantified in PET image biomarkers (PET-IBMs). PATIENTS AND METHODS: Intensity and textural PET-IBMs of the parotid gland were collected from pre-treatment 18F-FDG PET images of 161 head and neck cancerpatients. Patient-rated toxicity was prospectively collected. Multivariable logistic regression models resulting from step-wise forward selection and Lasso regularisation were internally validated by bootstrapping. The reference model with parotid gland dose and Xerbaseline was compared with the resulting PET-IBM models. RESULTS: High values of the intensity PET-IBM (90th percentile (P90)) and textural PET-IBM (Long Run High Grey-level Emphasis 3 (LRHG3E)) were significantly associated with lower risk of Xer12m. Both PET-IBMs significantly added in the prediction of Xer12m to the reference model. The AUC increased from 0.73 (0.65-0.81) (reference model) to 0.77 (0.70-0.84) (P90) and 0.77 (0.69-0.84) (LRHG3E). CONCLUSION: Prediction of Xer12m was significantly improved with pre-treatment PET-IBMs, indicating that high metabolic parotid gland activity is associated with lower risk of developing late xerostomia. This study highlights the potential of incorporating patient-specific PET-derived functional characteristics into NTCP model development.
Authors: Yevgeniy Vinogradskiy; Quentin Diot; Bernard Jones; Richard Castillo; Edward Castillo; Jennifer Kwak; Daniel Bowles; Inga Grills; Nicholas Myziuk; Thomas Guerrero; Craig Stevens; Tracey Schefter; Laurie E Gaspar; Brian Kavanagh; Moyed Miften; Chad Rusthoven Journal: Int J Radiat Oncol Biol Phys Date: 2020-01-23 Impact factor: 7.038
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
Authors: Kyle J Lafata; Yushi Chang; Chunhao Wang; Yvonne M Mowery; Irina Vergalasova; Donna Niedzwiecki; David S Yoo; Jian-Guo Liu; David M Brizel; Fang-Fang Yin Journal: Med Phys Date: 2021-06-02 Impact factor: 4.506
Authors: Hesham Elhalawani; Carlos E Cardenas; Stefania Volpe; Souptik Barua; Sonja Stieb; Calvin B Rock; Timothy Lin; Pei Yang; Haijun Wu; Jhankruti Zaveri; Baher Elgohari; Lamiaa E Abdallah; Amit Jethanandani; Abdallah S R Mohamed; Laurence E Court; Katherine A Hutcheson; G Brandon Gunn; David I Rosenthal; Steven J Frank; Adam S Garden; Arvind Rao; Clifton D Fuller Journal: Clin Transl Radiat Oncol Date: 2021-06-06