ESTRO 2020 Abstract Book

S826 ESTRO 2020

indicate a potentially increased cerebral radiosensitivity of the periventricular region (PVR) and a variable relative biological effectiveness (RBE) for proton therapy of the brain (Eulitz 2019, Harrabi 2019, Peeler 2018). The purpose of this study was to investigate the radiosensitivity of the PVR and the variability of clinical proton RBE using clinical data. Material and Methods All grade II and III glioma patients treated between 2014 and 2017 with (adjuvant) proton radio(chemo)therapy using the passive scattered technique to a total dose of 54- 60 Gy(RBE) were considered for analysis. Prospective follow-up included three-monthly contrast-enhanced T1- weighted MR imaging (fuMRI). Contrast enhancement (CE) diagnosed as treatment-related brain lesions (symptomatic or clinically silent) were traced back to the fuMRI of first appearance, delineated and deformably co- registered to the MRI and CT used for proton treatment planning (Fig 1A-D). Only lesions progressing over time, and/or those persisting for ≥ 6 months follow-up, and located outside the gross tumour volume were included in the analysis. A 4 mm expansion around the cerebral ventricles was contoured as PVR (Fig 1D). High-precision Monte-Carlo simulations (TOPAS) provided dose (D) and linear energy transfer (LET). CE spots were correlated voxel-wise using uni- and multivariable logistic regression analysis with the predictors D, LET and being in- or outside of the PVR. Additionally, area under the curve (AUC) and pseudo R-squared (pR 2 ) values were calculated for assessing the discrimination and prediction power of each model, respectively.

Conclusion In a cohort of grade II/III glioma patients, symptomatic or clinically silent contrast-enhancing lesions following proton therapy clustered in direct proximity to the ventricles and around the CTV boundary. The data indicate substantially increased cerebral radiosensitivity of the PVR as well as a variable proton RBE and suggest the need for adapted treatment planning. PO-1530 Pulmonary toxicity in lung cancer treated by (chemo)-radiotherapy : a radiomics-based NTCP. V. Bourbonne 1,2 , F. Lucia 2 , G. Dissaux 1,2 , B. Julien 2 , D. Visvikis 2 , O. Pradier 1,2 , M. Hatt 2 , U. Schick 1,2 1 CHRU Brest, Radiation Oncology, Brest, France ; 2 Univ Brest, LaTIM- UMR 1101- INSERM, Brest, France Purpose or Objective Purpose: (chemo) – radiotherapy is the gold standard therapeutic option for patients with locally advanced lung cancer non accessible or ineligible for surgery. Despite few advances in progression free survival and overall survival thanks to recents advances (i.e durvalumab), prediction of toxicities, namely lung toxicity and its inherent morbidity, remain insufficient. Current dose-volume histograms (DVH) do not account for spatial dose distribution and strict application of current dose constraints does not prevent serious toxicity for all treated patients. We aim to invest the added value of radiomics applied to dose maps to predict acute and late pulmonary toxicity. Material and Methods Methods: Acute and late toxicities scored using the CTCAE v4.0 were retrospectively collected on all patients treated by arctherapy-based (chemo)-radiotherapy in our institution. Radiomic features were extracted from 3D dose maps from homolateral, controlateral and both lungs. After selection of the features based on a ROC (receiver operating characteristic) analysis, dose distributions, clinical factors (age, performance status, AJCC stage, baseline respiratory function, histology type,…) and radiomic features were logistically combined in order to train three models a clinical, a clinical + DVH and a clinical + DVH + radiomics based models. These Normal-Tissue Complications Probability (NTCP) models were evaluated for prediction of clinically relevant pulmonary toxicity: acute pulmonary toxicity (APT), late pulmonary toxicity (LPT), radiation induced pneumonitis (RIP) treated by corticosteroids (RIPCTC) and RIP needing hospitalization (RIPHPT). Results Results: 167 patients were treated from 2015 to 2018: 38% squamous-cell carcinoma, 40% adenocarcinoma, 14% small cell, 8% other histology with a median age at treatment of 66 years. Respectively, 22.8%, 16.8%, 19.2%, 10.8% experienced an APT > grade 1, LPT > grade 1, an RIPCTC and an RIPHPT. Areas under the ROC curve (AUC) for APT, LPT, RIPCTC and RIPHPT were respectively of 0.63/0.65/0.82, 0.65/0.72/0.80, 0.62/0.69/0.82 and 0.63/0.79/0.85 when comparing the clinical, clinical + DVH and clinical + DVH + radiomics models. Radiomic features selected in the models were mostly those extracted from the ‘homolateral lung’ volume or the ‘both lungs’ volume.

- Results 39 patients fulfilling the inclusion criteria were included in the analysis and the median follow-up was 23 months. Median time between first fraction of radiotherapy and the earliest detection of a CE lesion was 19.3 months. The mean distances from the centre of the earliest detected lesion to the lateral cerebral ventricles and clinical target volume (CTV) were 2.3 mm and 2.6 mm, respectively (Fig 2). Voxels within lesions were significantly spatially correlated with D (AUC = 0.71), the PVR (0.72), and D×LET (0.75; p < 0.001, respectively). The multivariable combination of D and D×LET revealed an AUC and pR 2 value of 0.77 and 0.08, respectively. The model with D, PVR and D×LET as predictors resulted in the highest AUC and pR 2 values of 0.88 and 0.16, respectively, and the predicted probability for CE within an image voxel is illustrated in Fig 1F.