ESTRO 2021 Abstract Book

S1245

ESTRO 2021

1 University Hospital Heidelberg, Department of Radiation Oncology, Heidelberg, Germany; 2 University Hospital Heidelberg, Department of Nuclear Medicine, Heidelberg, Germany; 3 Molecular Imaging Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, USA; 4 University Hospital Heidelberg, Department of Diagnostic and Interventional Radiology, Heidelberg, Germany Purpose or Objective Adenoid cystic carcinomas (ACCs) are rare epithelial tumors mostly situated in the head and neck region and characterized by infiltrative growth. The tumor stroma of ACCs includes cancer-associated fibroblasts (CAFs) expressing Fibroblast Activation Protein (FAP), a new target for positron emission tomography (PET) imaging. Here we describe the value of PET/ computed tomography (PET/CT) imaging using 68 Ga-labelled FAP-Inhibitors ( 68 Ga-FAPI-PET/CT) and their clinical potential for staging and radiotherapy planning in 12 ACC patients (7 Patients underwent contrast enhanced CT (ceCT) and magnetic resonance imaging (ceMRI) before 68 Ga-FAPI - PET/CT. PET-scans were acquired 10, 60 and 180 minutes after administration of 150-250 MBq of 68 Ga-labelled FAPI tracers. SUV max and SUV mean values of ACCs and healthy organs were obtained using a 60% of maximum iso- contour. FAP and alpha smooth muscle actin (α-SMA) immunohistochemistry was performed in 13 cases (3 with and 10 without 68 Ga FAPI-PET/CT). Staging and radiotherapy planning based on 68 Ga-FAPI-PET/CT versus ceCT/MRI alone were compared. Results We observed elevated tracer uptake in all ACCs. Immunohistochemistry showed FAP-expressing CAFs in the tumor. Compared to conventional staging, 68 Ga-FAPI-PET/CT led to upstaging in 2/12 patients and to detection of additional metastases in 3 patients, thus in total 42% of patients had their staging altered. Moreover, 68 Ga- FAPI PET improved the accuracy of target volume delineation for radiotherapy, as compared to CT and MRI. Conclusion 68 Ga-FAPI-PET/CT is a promising imaging modality for ACC, providing additional information for staging and leading to altered radiotherapy planning compared to CT and MRI. PO-1521 External validation of a prediction model for timely implementation of innovations in radiotherapy. R. Swart 1 , M. Jacobs 2 , L. Boersma 1 , M. Behrendt 3 , M. Ketelaars 3 , C. Roumen 1 , R. Fijten 1 1 Stichting Maastricht Radiation Oncology, Department of Radiation Oncology , Maastricht, The Netherlands; 2 Tilburg University, Tilburg School of Economics and Management, Tilburg, The Netherlands; 3 Leiden University Medical Center, Department of Radiation Oncology , Leiden, The Netherlands Purpose or Objective A previously published internally validated model (built in Maastro and the Netherlands Cancer Institute) (Fig 1) was developed that can predict whether innovations in radiotherapy will be timely implemented or not. This model enables radiotherapy centres to secure the most critical and manageable success factors before the innovation projects starts and to implement innovations more successfully. The aim of this study was to externally validate the prediction model on a new dataset, in Maastro (new projects were included) and Leiden University Medical Center (LUMC), so it can be broadly used in radiotherapy. Materials and Methods A multivariate prediction model was built based on the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) criteria for a type 4 study (1). The previously built model had an AUC of 0.82, and was validated using a completely new dataset. Innovation projects, executed in the period 2018-2019, were included in this study. Semi-structured interviews were performed to retrieve the prognostic variables of the earlier built model. Projects were categorized according to the size of the project (hours spent on the project, number of project members and number of disciplines involved). The success of the project (timely, delayed or not completed) and the presence of pre-defined success factors were analysed. Results From the 58 innovation projects (33% technological, 29% organisation and 38% treatment innovations), that were included in the analyses, 66% was successfully implemented within the planned time frame. Comparing the outcome predictions with the observed outcomes of all innovations resulted in an Area Under the Curve (AUC) of the external validation of the prediction model of 0.72 (0.56 – 0.87, 95% confidence interval) (Fig 2). The size of the project had no significant impact on timely implementation. primary, 5 recurrent). Materials and Methods

Figure 1. Nomogram and regression formula to estimate the probability of successful implementation, based upon different characteristic of an innovation project.