ESTRO 36 Abstract Book

S265 ESTRO 36 2017 _______________________________________________________________________________________________

with no evidence of disease (death NED), and was censored in case of event-free at last follow-up. Three cause-specific Cox models were built using clinical, functional and morphological imaging input as candidate predictors, and using a cross validation technique to reduce the model to the prediction variables included in Table 1. Individualized estimates of 3-yr LRF, DM and death NED were obtained combining the three Cox regression models 1 , thus taking competing risks into consideration. The performance of the risk predictions was quantified by cause-specific concordance (C)-indices 2 (ideal C-index=1, coin flip C-index=0.5). The risk profiles of patients referred to an in-house dose escalation study were examined, as were the risk profiles of those of the 600 patients from model building which fulfilled the published inclusion criteria for the RTOG 1016 de-intensification trial. Results In the final analysis, 547 patients with complete data were included. The observed 3-year incidences were: LRF 25%, DM 10% and death NED 14%. Figure 1a presents a visualization of the individual risk of all patients (note that all probabilities add to 100%). The C-indices for the risk predictions were: LRF: 0.72, DM: 0.67, Death NED: 0.65. Of the 547 patients, 131 would have met the inclusion criteria of the RTOG 1016 de-escalation trial. The risk profiles of these patients (Figure 1b) show that 27 (21%) of them had an estimated risk of failure (LRF and DM) exceeding 20%. Of the 15 patients included in our local dose escalation study, 11 had a risk of loco-regional failure of less than 20% (Figure 1c).

Conclusion Our findings suggest that PD-L1 may serve as a promising biomarker for poor prognosis as well as risk stratification and even therapeutic targets in HNSCC. Further well- designed studies and long-term follow up are warranted to verify these results. PV-0509 Failure type specific prognostic model for selection of HNSCC patients for experimental treatments K. Håkansson 1 , J.H. Rasmussen 2 , G.B. Rasmussen 1 , J. Friborg 1 , T.A. Gerds 3 , S.M. Bentzen 4,5 , L. Specht 1 , I.R. Vogelius 1 1 Rigshospitalet- University of Copenhagen, Department of Oncology- Section of Radiotherapy, Copenhagen, Denmark 2 Rigshospitalet- University of Copenhagen, Department of Otorhinolaryngology- Head & Neck Surgery and Audiology, Copenhagen, Denmark 3 University of Copenhagen, Department of Biostatistics, Copenhagen, Denmark 4 University of Maryland Greenebaum Cancer Center, Division of Biostatistics and Bioinformatics, Baltimore, USA 5 University of Maryland School of Medicine, Department of Epidemiology and Public Health, Baltimore, USA Purpose or Objective Most clinical trials involve simple inclusion/exclusion criteria without support by prognostic models. Here, we present a multivariate model on multiple endpoints to generate an individual risk profile. We then examine the risk profile of patients actually referred to a dose escalation trial and patients that would be candidates for the RTOG 1016 de-intensification trial. Material and Methods Data from 600 HNSCC patients receiving intensity- modulated radiotherapy at our institution from 2005-2012 were retrospectively analyzed. Outcome was time from start of radiotherapy to the first occurrence of loco- regional failure (LRF), distant metastasis (DM) or death

Conclusion The prediction model performed well for LRF, but death NED and DM risk were only moderately well predicted. Using the model to examine the profile of patients that are candidates for a de-intensification schedule, we document that several patients at a relatively high risk of failure could be included.Conversely, our own dose escalation study included several low risk patients, despite focusing on p16 negative patients or heavy