Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models.The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes.This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team’s consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines.

Purpose/Objective(s) Image biomarkers on planning PET/CT-scans are potential prognostic tools in patient-specific predisposition to first site of failure in the treatment of locally advanced NSCLC-patients (LA-NSCLC) with curative intended chemo-/radiotherapy (cCRT). Materials/Methods Patients treated with cCRT for LA-NSCLC at a single institution from 2012-2018 were retrospectively included (N=197). Planning PET/CT scans were analyzed (commercially available software) with respect to selected image biomarkers (volume and sphericity of GTV-T on CT and SUVpeak and percentage of GTV-T volume with PET-signal above 50% of SUVpeak on PET). Clinical baseline characteristics (gender, stage, histology, performance status (PS)) were collected. Progression was scored as either loco-regional (LR), distant metastasis (M), simultaneous loco-regional and distant (LR+M) or death with no evidence of disease (DNED). Data were analyzed with a Fine and Gray competing risk analysis and variables included are shown in table 1. For each failure mode, subdistributed hazard ratios (sHR) with 95%-confidence intervals are reported. Results Median follow-up-time was 24 months. Significant predictors of LR failure were histology (squamous cell carcinoma (SCC) vs. adenocarcinoma (AC), sHR=2.05 [1.02-4.13], p=0.045) and SUVpeak (sHR=1.079 pr. SUV-increase [1.01-1.16], p=0.03). Histology remained a significant predictor for M failure (sHR=0.195 [0.07-0.52], p<0.01). No significant predictive parameters for LR+M-failure or DNED were found. Comparing cumulative incidences between the four groups (divided by histology and SUVpeak above/below median) using Fine-Gray test showed significant differences between the four groups in terms of LR-failure (P<0.01), M-failure (p<0.01) and LR+M-failure (p<0.01). LR-failures 2 years after treatment were similar for SCC regardless of SUVpeak (37% vs. 32%). Meanwhile, AC with SUVpeak above median had a significantly higher risk of LR-failure after 2 years than AC with SUVpeak below median (27% vs. 8%). M-failure after 2-years varied with histology but not with SUVpeak (AC: 35% vs. 28%, SCC: 9% vs. 5%). Conclusion In a competing risk analysis of 197 LA-NSCLC patients treated with cCRT, histology and SUVpeak prior to cCRT were identified as significant predictors of LR-failure. Patients with AC displayed a lower risk of LR-failure if SUVpeak was below median, which separated this group from the remaining patients. In general, AC were significantly more prone to M-failure than SCC.

Locally advanced NSCLC (LA-NSCLC) is treated with the same chemoradiotherapy strategy irrespective if the histology is adenocarcinomas (AC) or squamous cell carcinomas (SCC). However, they differ in terms of pattern of failure with AC being more prone to distant failure (D) and SCC more prone to loco-regional failure (LR). Recent study [1] has shown improved overall survival (OS) and progression free survival (PFS) using adaptive radiotherapy (ART). This study investigates if patients with different histology experience different benefits of ART.

Background Lung cancer patients struggle with high toxicity rates. This study investigates if IMRT plans with individually set beam angles or uni-lateral VMAT plans results in dose reduction to OARs. We investigate if introduction of a RapidPlan model leads to reduced dose to OARs. Finally, the model is validated prospectively. Material and methods Seventy-four consecutive lung cancer patients treated with IMRT were included. For all patients, new IMRT plans were made by an experienced dose planner re-tuning beam angles aiming for minimized dose to the lungs and heart. Additionally, VMAT plans were made. The IMRT plans were selected as input for a RapidPlan model, which was used to generate 74 new IMRT plans. The new IMRT plans were used as input for a second RapidPlan model. This model was clinically implemented and used for generation of clinical treatment plans. Dosimetric parameters were compared using a Wilcoxon signed rank test or a 1-sided student's t-test. p < .05 was considered significant. Results IMRT plans significantly reduced mean doses to lungs (MLD) and heart (MHD) by 1.6 Gy and 1.7 Gy in mean compared to VMAT plans. MLD was significantly (p < .001) reduced from 10.8 Gy to 9.4 Gy by using the second RapidPlan model. MHD was significantly (p < .001) reduced from 4.9 Gy to 3.9 Gy. The model was validated in prospectively collected treatment plans showing significantly lower MLD after the implementation of the second RapidPlan model. Conclusion Introduction of RapidPlan and beam angles selected based on the target and OARs position reduces dose to OARs.