S. Fergus

WE‐E‐BRE‐09: Investigation of the Association Between Radiation‐Induced Pain and Radiation Dose in Head and Neck Cancer Patients

PURPOSE Patients with head and neck cancer who undergo radiotherapy often experience several undesirable side-effects, including xerostomia, trismus, and pain in the head and neck area, but little is know about the dose-volume predictors of such pain. We investigated the association between radiation dose and both throat and esophagus pain during radiotherapy. METHODS We analyzed 124 head and neck patients who received radiotherapy at the Washington University School of Medicine in Saint Louis. For these patients, weekly PROs were recorded, including 16 pain and anatomical location questions. In addition, 17 observational symptoms were recorded. Patients were asked to describe their pain at each site according to a four-level scale: none (0), mild (1), moderate (2), and severe (3). We explored the association between throat pain and the mean dose received in oral cavity and between esophageal pain and the mean dose received in the esophagus. The severity of pain was determined by the difference between the baseline (week 1) pain score and the maximum pain score during treatment. The baseline pain score was defined as the first available pain score before receiving 10 Gy because radiotherapy pain originates later during treatment. Dose-volume metrics were extracted from treatment plans using CERR. To evaluate the correlation between pain and radiation dose, Spearmans correlation coefficient (Rs) was used. RESULTS The associations between throat pain and the mean dose to the oral cavity, and between esophagus pain and the mean dose to the esophagus, were both statistically significant, with Rs=0.320 (p=0.003) and Rs=0.424 (p<0.0001), respectively. Mean dose, for each structure, was a better predictor of pain than total integral dose. CONCLUSION We demonstrated that pain during radiotherapy in head and neck patients highly correlates with the dose delivered. We will further investigate the association between other pain locations and relevant normal tissue dose characteristics.

SU‐FF‐J‐20: A Multimodality Imaging Approach for Predicting Radiation Induced Lung Injury

Purpose:Radiation pneumonitis (RP), is a major dose limiting toxicity in thoracic irradiation for patients with lung or breast cancer. Multimodality imaging and particularly integrated PET/CT imaging is increasing used in radiotherapytreatment staging, planning, and monitoring. In this work, we are investigating the potential of a new hybrid feature‐based approach utilizing post‐radiotherapy PET/CT images to predict the risk of later RP. Method and Materials: As a demonstrative example for analyzing normal tissue toxicities using the proposed multimodality approach, we analyzed post‐treatment PET/CT scans (typically, 3 mos. post‐RT) of 20 lungcancerradiotherapy patients for the endpoint of pneumonitis. The rate of RP in this group was 20%. The lung region minus the Gross Tumor Volume (GTV) was designated as the region of interest (ROI). We extracted features based on descriptive statistics of intensity (in units of SUV in PET and Hounsfield in CT),image intensity volume histogram (IVH) metrics, and texture‐based features. Statistical association was performed using Spearmans rank correlation (Rs). Results: Thirty candidate features were extracted from each image modality. Preliminary results indicate that texture features in both PET and CT seems to predict RP. For instance, the PET ROI standard deviation had Rs=0.14 in PET and Rs=−0.035 in CT while the texture (related to roughness) local heterogeneity had Rs=0.33 (p=0.08) in PET and Rs=0.63 (p=0.002) in CT.Conclusion: We have proposed a new approach for predicting RP risk pneumonitis from hybrid PET/CT image features. Texture related features on the post‐RT CT scan provide the best prediction. Estimating of these features could provide the physician with an early warning enabling a more proactive management of RP symptoms. Further analysis of these multimodality features, their complementary effect, and the optimal time of imaging, is needed to fully understand this finding. Partially supported by K25 CA128809 and R01 CA85181.