ESTRO 2020 Abstract book

S225 ESTRO 2020

SP-0389 The role of screening and prevention after radiotherapy J. Chang-Claude 1 1 German Cancer Research Center DKFZ, Division of Cancer Epidemiology, Heidelberg, Germany Abstract text Survival of cancer patients, particularly childhood and young adult cancers, has improved with earlier detection of cancers and better treatment modalities. Yet longer survival is related to a greater risk of developing second malignancies, which is the most serious potential treatment sequelae. Thus, second malignancies have increased from 7% in 1990 to 18% in 2016 in the Netherlands, respectively 17% in the SEER data. Earlier estimates of about 8% in all patients treated with radiotherapy could thus have similarly increased. Radiotherapy during childhood or young adulthood is known to double the risk for breast cancer compared to the general population. Survivors of Hodgkin’s lymphoma will generally develop second cancers at an earlier age than the general population. Therefore early detection and treatment of therapy-related malignancies has become a priority even if new radiation techniques will lead to better protection of organs at risk. This talk will provide an overview of risks for second malignancies, international guidelines for cancer surveillance and current efforts. SP-0390 Normal tissue complication modelling for improved radiotherapy planning R. Kierkels 1,2 1 University Medical Center Groningen, Department Of Radiation Oncology, Groningen, The Netherlands ; 2 radiotherpiegroep, Department Of Radiation Oncology, Deventer/Arnhem, The Netherlands Abstract text Normal tissue complication probability (NTCP) models describe a relationship between radiotherapy dose and the clinical effect. In general, NTCP models are a combination of biological knowledge and clinical data. Traditional NTCP models utilizing the Lyman-Butcher-Burman (LKB) formula in which the generalized Equivalent Uniform Dose (gEUD) is embedded to describe heterogeneous dose distributions within a single organ at risk (OAR). However, it is presumed that complications are related to multiple factors of which the biological knowledge is missing. Therefore, multivariable NTCP models are increasingly based on clinical, dosimetry, or genetic data. These logistic regression models generally lead to improved prediction accuracy and are increasingly used for decision making and optimization. Traditional treatment planning optimization commonly utilizes dose-volume based objectives and constraints to optimize the dose distribution. Single objectives are generally wrapped into a quadratic function and summed. This type of composite objective function does not describe the clinical impact of the resulting dose distribution explicitly. As an intermediate approach, the most commonly used commercial treatment planning systems implemented the gEUD formula (with a tissue sensitivity parameter a ) as a biologically motivated objective function. Although the gEUD can directly be Symposium: Radiobiological guidance for treatment planning

converted into an NTCP using the LKB model, more sophisticated multivariable NTCP models would be more clinically meaningful than the gEUD. Therefore, treatment plan optimization should be facilitated by multivariable NTCP models. The knowledge captured by NTCP models can be used to optimize treatment plans for novel patients. The flow chart in figure 1 illustrates a typical learning cycle in which NTCP models are trained and used for treatment plan optimization. A straightforward approach to optimize a dose distribution by means of NTCPs, is to directly translate multivariable NTCP models into an objective function, i.e., minimizing the objective value (i.e. the NTCP) directly by the optimizer. We have automatically optimized dose distributions for head and neck cancer patients with multivariable NTCP models for various clinical end-points, such as xerostomia and dysphagia. We demonstrated that multivariable NTCP-based objective functions combined with dose-volume objectives for the targets results in clinically realistic treatment plans. In addition, the NTCP-based objectives facilitates automated optimization, in which the search for a traditional dose threshold value was omitted and replace by the multivariable NTCP model directly. This approach is, however, not supported by the commonly used commercial treatment planning systems. An alternative approach is, therefore, to translate the dose-volume parameters from the NTCP models into traditional dose-volume objectives. The regression coefficients of the logistic regression coefficients of the multivariable NTCP models provide inside into the importance (i.e. the relative weight) of a particular OAR. Since 2007, the latter approach was followed in our clinic. The first years (2007-2011), 201 patients were treated with standard parotid sparing IMRT (ST-RT), while maintaining objectives and constraints for the target volumes and serial-like organs. The following years (2012-2017), 704 patients were treated with so-called swallowing sparing VMAT (SW-RT). Since 2017, 213 patients were treatment with so-called oral cavity sparing treatments using either VMAT or IMPT (OCS-RT). Following two cycles of the NTCP- guided optimization loop (figure 1), on average, the oral cavity D mean decreased significantly from 48.5 Gy (±15.7) with ST-RT to 27.7 Gy (±16.3) with OCS-RT (p<0.001). The D mean of the superior pharyngeal constrictor muscle significantly decreased from 57.6 Gy (±13.8) to 41.5 Gy (±18.9) from ST-RT to OCS-RT, respectively (p<0.001). Consequently, significant reduction in grade≥2 and grade≥3 physician-rated and patient-rated dysphagia as compared to PS-RT were reported 6 months after treatment.