Abstract Book

ESTRO 37

S536

Figure 2. a) Dose distribution calculated with TOPAS on the ground truth numerical phantom. Relative error on absorbed dose using b) SECT tissue segmentation and c) DECT with realistic noise level. Conclusion A recently published method for tissue characterization with DECT is successfully implemented in TOPAS. Dose distributions calculated with MC for single proton beams in a highly realistic phantom shows a noticeably improved proton range prediction using DECT over SECT for simulated images affected by noise.

ratio test p=0.005). The addition of XERw3 further improved the performance to an AUC of 0.90 (Model 2 Table). Parotid gland dose did not significantly add to the ΔIBM models (log-likelihood ratio test; p>0.06).

Poster: Physics track: (Quantitative) functional and biological imaging

PO-0972 Geometric image biomarker changes at the third treatment week predict late xerostomia T.A. Vedelaar 1 , L.V. Van Dijk 1 , T. Zhai 1 , C.L. Brouwer 1 , J.A. Langendijk 1 , R.J.H.M. Steenbakkers 1 , N.M. Sijtsema 1 1 University Medical Center Groningen, Department of Radiation oncology, Groningen, The Netherlands Purpose or Objective Radiation-induced xerostomia is a common toxicity of the treatment which has a major impact on the quality of life of head and neck cancer (HNC) patients. Parotid gland response to radiation dose during and after treatment varies between different patients. Mid-treatment delta image biomarkers (ΔIBMs) of the parotid gland that predict the risk on xerostomia could contribute to the selection of patients in order to adapt treatment and potentially reduce the probability of developing late xerostomia. The aim of this study is to predict xerostomia 12 months after treatment (XER12m) with ΔIBMs at the beginning of the 3rd treatment week in combination with dosimetric, clinical and treatment parameters. Material and Methods Geometric IBMs (20) were calculated of 57 patients on the CT images acquired before treatment (planning CT) and on a repeat CT acquired at the beginning of 3rd treatment week. The difference between both IBMs resulted in geometric ΔIBMs. Additionally, xerostomia scores before start (XERbaseline) and at the beginning of the 3rd treatment week (XERw3), and mean dose to the contralateral parotid gland were considered as possible predictors of XER12m. To select the best predictors, a stepwise forward selection based on the likelihood test (p<0.05) was performed. This process has been bootstrapped 1000 times. The final logistic multivariable models were compared with a reference model based on parotid gland dose and baseline xerostomia scores. All models were corrected for baseline xerostomia scores and internally validated. Results Twenty-four (42%) patients developed moderate-to- severe XER12m. The contralateral parotid glands surface change (ΔSurface) was the most frequently selected ΔIBM. ΔSurface was significantly associated to XER12m (p<0.001) and added significantly to an univariable model with XERbaseline (likelihood-ratio test; p =0.001). The performance of the ΔIBM model with ΔSurface and XERbaseline (AUC=0.87) was high and improved compared to the reference model (AUC=0.84) (Model 1 Table). Furthermore, the addition of XERw3 to this ΔIBM model (XERbaseline and ΔSurface) was significant (likelihood

Conclusion The ΔIBM parotid gland surface change (ΔSurface) between the 3rd week during and start of treatment was associated with the development of XER12m. The ΔIBM model with ΔSurface and XERbaseline improved the performance (AUC = 0.87) with respect to the reference model (AUC = 0.84). Addition of XERw3 further improved the performance of this ΔIBM model (AUC = 0.90). PO-0973 SUVpeak based segmentation to determine lung tumour volume on FDG PET-CT compared with pathology S. Mercieca 1 , J. Belderbos 2 , J. Van Loon 3 , K. Gilhuijs 4 , P. Julyan 5 , M. Van Herk 6 1 University of Malta, Department of Radiography, Msida, Malta 2 The Netherlands Cancer Institute Antoni van Leeuwenhoek Hospital, Radiotherapy Department, Amsterdam, The Netherlands 3 Maastro Clinic-, Department of Radiation Oncology, Maastricht, The Netherlands 4 University Medical Centre, Department of Medical Physics, Utrecht, The Netherlands 5 The Christie NHS Foundation Trust, Nuclear Medicine