ESTRO 2021 Abstract Book

S714

ESTRO 2021

Fuller 6 1 Hunan Cancer Hospital, Radiation Oncology Department, Changsha, China; 2 Yueyang 2nd People`s Hospital , Radiation Oncology Deparment, Yueyang, China; 3 Hunan Cancer Hospital, Department of Radiation Oncology, Changsha, China; 4 Central South University , School of Computer Science and Engineering, Changsha, China; 5 Brigham and Women's Hospital, Department of Radiation Oncology, Boston, USA; 6 The University of Texas MD Anderson Cancer Center , Department of Radiation Oncology, houston, USA Purpose or Objective Due to the differences in patients' lung conditions, radiation pneumonia (RP) sometimes occurs even if the lung's dose limitation is met. We are aiming to further predict the occurrence odd of radiation pneumonitis in NSCLC after receiving radiotherapy. Consider based on the dose-volume level to segment the lungs receiving radiation, use radiomic modeling, and evaluate its predictive effectiveness. Materials and Methods The study retrospectively enrolled 306 CT scans from 102 NSCLC patients who received IMRT or VMAT from Oct 2015 to Aug 2016. A 0-5 grade RP occurred within 12 months after radiotherapy, considered as the endpoint event. The evaluation of radiation pneumonia in this study case was based on CTCAE 5.0. The whole cohort was divided into two groups, the RP and non-RP. The lung was segmented based on the dose-volume line as V5-10, V10-20, V20-30, V30-40, V40-50, and V50-60 as the region of interest (ROI). Then we register the ROI to the lung of the CT images in the middle and after the radiotherapy. We utilize Pyradiomics to extract the image features for each ROI to develop and validate the radiomics model to predict radiation pneumonitis. Two classifier models of random forest (Random forest, RF) and support vector machine (SVM) were used for classifying and learning samples' characteristics to distinguish between the pneumonia-occurring group and the non-occurring group effectively. We randomly divided the sample size into a training and validation group at a 4:1 ratio, and the five-fold cross-validation model (K fold cross-validation) was used for verification. The predictive performance was evaluated using overall accuracy for this triple classification task. Results The overall incidence of radiation pneumonia was 36%, and the incidence of severe radiation pneumonia (grade 3 and above) was 4%. The support vector machine (SVM) model has the best prediction performance compare to randomforest, with an average accuracy rate of 0.72 and an average AUC value of 0.66. Under different dose line volume segmentation, the accuracy of the prediction models in pre-, mid-, and after treatment are 0.724, 0.744, 0.775 (V5-10), 0.736, 0.719, 0.737, (V10-20), 0.728, 0.696, 0.693 (V20-30), 0.671, 0.678, 0.684 (V30-40), 0.747, 0.724, 0.705 (v40-50), 0.799, 0.743, 0.726 (V50-60), respectively. The model constructed in the 50-60Gy area of CT before radiotherapy has the best performance in predicting radiation PD-0878 Reduction of post-operative toxicity in esophageal cancer patients after model based proton therapy C. Muijs 1 , M. Dieters 1 , V.E. Mul 1 , A.G. Niezink 1 , A. Schaaf van de 1 , N.M. Sijtsema 1 , S. Visser 1 , P. Klinker 1 , B. Etten van 2 , E. Korevaar 3 , J.A. Langendijk 1 1 University Medical Center Groningen / University of Groningen, Radiation Oncology, Groningen, The Netherlands; 2 University Medical Center Groningen / University of Groningen, Surgical Oncology, Groningen, The Netherlands; 3 University Medical Center Groningen / University of Groningen, Radiation Oncology, Groningen , The Netherlands Purpose or Objective During the COVID-19 pandemic, health insurance providers permitted to treat esophageal cancer (EC) patients with proton therapy (PT) when treated in the neo-adjuvant setting. This decision was based on the results of the MD Anderson randomized study showing significantly lower rates of the Total Toxicity Burden (TTB), especially of postoperative toxicity and consequently decreased hospital and intensive care unit (ICU) stay. The TTB combines a large range of toxicities with different incidence rates and severities into a single sum score. For patient selection, we used a model-based approach (MBA) using a model for TTB (≥60). The aim of this study was to investigate if the post-operative TTB decreased with PT. Materials and Methods In accordance with the MBA, a plan comparison (photon vs proton radiotherapy (RT)) was performed in all EC patients that were to be treated with neo-adjuvant chemoradiotherapy (nCRT) according to the CROSS regimen. For patient selection, we developed a normal tissue complication probability (NTCP)-model for a TTB ≥ 60, which corresponds to at least one grade ≥ III or 2 grade ≥ II complications. Patients, who were eligible for PT in terms of target motion (<15 mm), and with a ΔNCTP >5%, were selected and treated with PT. Post- operative TTB and hospital and ICU stay were compared with a prospective dataset of patients that were treated with photon RT between 2014 and 2018 (n=224). Results Since March 2020, 26 out of 32 patients were selected for PT. The average reduction in mean lung dose was 5.2 Gy with PT compared to VMAT, with a corresponding average NTCP-reduction for TTB of 9.7%. Mean heart, spleen and liver dose were reduced by 7.9, 7.4 and 7.1 Gy, respectively. At the time of analyses, 14 out of 19 patients underwent esophagectomy. Three patients developed intercurrent metastases, in one a wait and see policy was applied and one switched to definitive CRT. pneumonia. Conclusion Radiation pneumonia is a side effect that needs to be paid attention to in thoracic radiotherapy, and its incidence and severity directly affect patients' survival. Traditional radiation pneumonia prediction methods are relatively general. With the development of precise radiotherapy, further accurate radiation pneumonia prediction models are required. Radiomics can become a reliable prediction method for radiation pneumonia Poster discussions: Poster discussion 26: Upper GI (oesophagus, stomach) 1