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Objective Since the end of 2019, the coronavirus disease 2019 (COVID-19) virus has infected millions of people, of whom a significant group suffers from sequelae from COVID-19, termed long COVID. As more and more patients emerge with long COVID who have symptoms of fatigue, myalgia and joint pain, we must examine potential biomarkers to find quantifiable parameters to define the underlying mechanisms and enable response monitoring. The aim of this study is to investigate the potential added value of [ 18F]FDG-PET/computed tomography (CT) for this group of long COVID patients. Methods For this proof of concept study, we evaluated [ 18F]FDG-PET/CT scans of long COVID patients and controls. Two analyses were performed: semi-quantitative analysis using target-to-background ratios (TBRs) in 24 targets and total vascular score (TVS) assessed by two independent nuclear medicine physicians. Mann-Whitney U-test was performed to find significant differences between the two groups. Results Thirteen patients were included in the long COVID group and 25 patients were included in the control group. No significant differences (P 18F]FDG is of added value for long COVID patients with symptoms of myalgia or joint pain. Prospective cohort studies are necessary to understand the underlying mechanisms of long COVID.

Positron emission tomography (PET) is an important imaging modality for personalizing clinical management of patients with lung cancer. In this regard, PET imaging is essential for adequate clinical staging and monitoring of treatment response in patients with lung cancer. The key advantage of PET over other radiological imaging modalities is its high sensitivity for the detection of pulmonary lesions, normal-sized metastatic hilar and/or mediastinal lymph nodes, and distant metastases. Furthermore, with increasing clinical evidence, the role of PET imaging for treatment selection, adaptation, early response mon-itoring and follow up in patients with lung cancer is being increasingly recognized. At the heart of PET imaging lies the ability to visualize and quantify numerous biological parame-ters that are responsible for treatment resistance. In order to ensure accurate and repro-ducible image quantification, harmonization of patient preparation and imaging protocols is essential. Additionally, there are several technical factors during PET scanning that have to be taken care of to safeguard image quality and quantitative accuracy. One of these factors is the occurrence of respiratory motion artifacts, which is a well-known fac-tor that can significantly influence image quality and quantitative accuracy of PET images. If left uncorrected, respiratory motion artifacts can introduce uncertainties in diagnosis and staging, inaccuracies in definition of target volumes for radiation treatment planning, and hinder adequate monitoring of therapy response. Although many different respiratory gating techniques have been developed to correct PET images for respiratory motion arti-facts, respiratory gating has traditionally not been widely adopted in clinical practice. This is due to the fact that these methods tend to be disruptive for the clinical workflow due the lengthening of image acquisition times, higher amounts of activity being administered to the patient, and the requirement to synchronize additional hardware with the scanner. Developments in respiratory gating techniques over the last years have resulted in considerable technical improvements. These newer respiratory gating techniques can operate directly on the acquired PET data without the use of additional hardware to trace respiratory motion and can be seamlessly applied into clinical routine. Furthermore, instead of only using a fraction of the acquired PET data newer methods have the ability to use all of the acquired PET data for image reconstruction, thereby improving image quality. The clinically added value of respiratory gating lies in improving image quality by reducing the amount of respiration-induced image blurring. This considerably improves the detection and characterization of small lesions, potentially improving early diagnosis and staging of patients with lung cancer. Furthermore, the incorpo-ration of (4D) respiratory gated PET for radiotherapy purposes has shown to improve target vol-ume definition through more accurate tracking of tumor motion. In addition, the effect of respiratory motion artifacts on widely used volumetric and uptake parameters in PET have been described. Although respiratory gating improves quantitative accuracy of PET images, the exact impact of these corrections on clinical management of patients with lung cancer often still needs to be determined.

Background: Accuracy and precision assessment in radiomic features is important for the determination of their potential to characterize cancer lesions. In this regard, simulation of different imaging conditions using specialized phantoms is increasingly being investigated. In this study, the design and evaluation of a modular multimodality imaging phantom to simulate heterogeneous uptake and enhancement patterns for radiomics quantification in hybrid imaging is presented. Methods: A modular multimodality imaging phantom was constructed that could simulate different patterns of heterogeneous uptake and enhancement patterns in positron emission tomography (PET), single-photon emission computed tomography (SPECT), computed tomography (CT), and magnetic resonance (MR) imaging. The phantom was designed to be used as an insert in the standard NEMA-NU2 IEC body phantom casing. The entire phantom insert is composed of three segments, each containing three separately fillable compartments. The fillable compartments between segments had different sizes in order to simulate heterogeneous patterns at different spatial scales. The compartments were separately filled with different ratios of Tc-99m-pertechnetate, F-18-fluorodeoxyglucose ([F-18]FDG), iodine- and gadolinium-based contrast agents for SPECT, PET, CT, and T-1-weighted MR imaging respectively. Image acquisition was performed using standard oncological protocols on all modalities and repeated five times for repeatability assessment. A total of 93 radiomic features were calculated. Variability was assessed by determining the coefficient of quartile variation (CQV) of the features. Comparison of feature repeatability at different modalities and spatial scales was performed using Kruskal-Wallis-, Mann-Whitney U-, one-way ANOVA- and independent t-tests. Results: Heterogeneous uptake and enhancement could be simulated on all four imaging modalities. Radiomic features in SPECT were significantly less stable than in all other modalities. Features in PET were significantly less stable than in MR and CT. A total of 20 features, particularly in the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) class, were found to be relatively stable in all four modalities for all three spatial scales of heterogeneous patterns (with CQV < 10%). Conclusion: The phantom was suitable for simulating heterogeneous uptake and enhancement patterns in [F-18]FDG-PET, Tc-99m-SPECT, CT, and T-1-weighted MR images. The results of this work indicate that the phantom might be useful for the further development and optimization of imaging protocols for radiomic quantification in hybrid imaging modalities.

423 Background: Despite the advent of precision medicine, prediction of survival outcome of esophageal cancer patients remains a challenge. Here we aim to investigate the value of prediction models integrating multi-signal data including radiomics and circulating tumor DNA (ctDNA) data in addition to clinical data for the prediction of resectable esophageal adenocarcinoma (rEAC) related outcomes. Methods: In total n=111 rEAC patients treated with neoadjuvant chemoradiotherapy (nCRT; n=71) +/- anti-PD-L1 (n=40) were included. Baseline clinical variables (n=9) were based on the SOURCE survival prediction model (van den Boorn et al. JNCCN. 2021). The baseline ctDNA data from plasma was derived from fragmentomic and copy number aberrations (ichorCNA) from shallow whole genome sequencing (0.6). We evaluated the predictive performance of baseline ctDNA and radiomics features on overall survival (OS), progression free survival (PFS), and time to progression (TTP), through fitting Cox-regression models. Four ctDNA features were included in the models: P20-150, ichorCNA, fragment end score and mutation detection. For the radiomics features we performed an additional back- and forward variable selection based on Akaike’s Information Criterion. Using the likelihood ratio test we tested if the model fit changed after adding ctDNA and radiomics features to a model with clinical variables. Results: The addition of radiomics to clinical variables improved model fit for OS (p=0.017). Baseline prediction of OS resulted in a C-index of 0.65 using clinical variables only, 0.65 with ctDNA, 0.68 with radiomics and 0.68 with ctDNA and radiomics combined. For PFS model fit improved after adding radiomics (p=0.020) and ctDNA and radiomics combined (p=0.017). Baseline prediction of PFS resulted in a C-index of 0.64 using clinical variables, 0.65 with ctDNA, 0.67 with radiomics, and 0.68 with ctDNA and radiomics combined. For TTP model fit improved after adding radiomics (p=0.008) and radiomics and ctDNA combined (p=0.002). Baseline prediction of TTP resulted in a C-index of 0.64 with clinical variables, 0.65 with ctDNA, 0.71 with radiomics, and 0.72 with ctDNA and radiomics combined. Based on the cox-regression models using clinical variables and radiomics, risk stratification by splitting the cohort in a high and low risk group was possible for OS, PFS and TTP (p