Abstract: Purpose To develop a QA procedure, easy to use, reproducible and based on open-source code, to automatically evaluate the stability of different metrics extracted from CT images: Hounsfield Unit (HU) calibration, edge characterization metrics (contrast and drop range) and radiomic features. Methods The QA protocol was based on electron density phantom imaging. Home-made open-source Python code was developed for the automatic computation of the metrics and their reproducibility analysis. The impact on reproducibility was evaluated for different radiation therapy protocols, and phantom positions within the field of view and systems, in terms of variability (Shapiro-Wilk test for 15 repeated measurements carried out over three days) and comparability (Bland-Altman analysis and Wilcoxon Rank Sum Test or Kendall Rank Correlation Coefficient). Results Regarding intrinsic variability, most metrics followed a normal distribution (88% of HU, 63% of edge parameters and 82% of radiomic features). Regarding comparability, HU and contrast were comparable in all conditions, and drop range only in the same CT scanner and phantom position. The percentages of comparable radiomic features independent of protocol, position and system were 59%, 78% and 54%, respectively. The non-significantly differences in HU calibration curves obtained for two different institutions (7%) translated in comparable Gamma Index G (1 mm, 1%, >99%). Conclusions An automated software to assess the reproducibility of different CT metrics was successfully created and validated. A QA routine proposal is suggested

Abstract: Background Patient's breathing affects the quality of chest images acquired with positron emission tomography/computed tomography (PET/CT) studies. Movement correction is required to optimize PET quantification in clinical settings. We present a reproducible methodology to compare the impact of different movement compensation protocols on PET image quality. Static phantom images were set as reference values, and recovery coefficients (RCs) were calculated from motion compensated images for the phantoms in respiratory movement. Image quality was evaluated in terms of: (1) volume accuracy (VA) with the NEMA phantom; (2) concentration accuracy (CA) by six refillable inserts within the electron density CIRS phantom; and (3) spatial resolution (R) with the Jaszczak phantom. Three different respiratory patterns were applied to the phantoms. We developed an open-source package to automatically analyze VA, CA and R. We compared 10 different movement compensation protocols available in the Philips Gemini TF-64 PET/CT (4-, 6-, 8- and 10-time bins, 20%-, 30%-, 40%-window width in Inhale and Exhale). Results The homemade package provided RC values for VA, CA and R of 102 PET images in less than 5 min. Results of the comparison of the 10 different protocols demonstrated the feasibility of the proposed method for quantifying the variations observed qualitatively. Overall, prospective protocols showed better motion compensation than retrospective. The best performance was obtained for the protocol Exhale 30% (0.3 s after maximum Exhale position and window width of 30%) with RCVA=1.6±1.3 , RCCA=0.90±0.09 and RCR=0.6±0.4 . Among retrospective protocols, 8 Phase protocol showed the best performance. Conclusion We provided an open-source package able to automatically evaluate the impact of motion compensation methods on PET image quality. A setup based on commonly available experimental phantoms is recommended. Its application for the comparison of 10 time-based approaches showed that Exhale 30% protocol had the best performance. The proposed framework is not specific to the phantoms and protocols presented on this study

Abstract: Purpose The value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-positron emission tomography (PET)-radiomics in the outcome assessment of patients with recurrent glioblastoma (rGBM) has not been evaluated until now. The aim of this study was to evaluate whether a prognostic model based on FET-PET radiomics features (RF) is feasible and can identify rGBM patients that would most benefit from re-irradiation. Methods We prospectively recruited rGBM patients who underwent FET-PET before re-irradiation (GLIAA-Pilot trial, DRKS00000633). Tumor volume was delineated using a semi-automatic method with a threshold of 1.8 times the standardized-uptake-value of the background. 135 FET-RF (histogram parameters, shape and texture features) were extracted. The analysis involved the characterization of tumor and non-tumor tissue with FET-RF and the evaluation of the prognostic value of FET-RF for time-to-progression (TTP), overall survival (OS) and recurrence location (RL). Results Thirty-two rGBM patients constituted our cohort. FET-RF discriminated significantly between tumor and non-tumor. The texture feature Small-Zone-Low-Gray-Level-Emphasis (SZLGE) showed the best performance for the prediction of TTP (p = 0.001, satisfying Bonferroni-multiple-test significance level). Additionally, two radiomics signatures could predict TTP (TTP-radiomics-signature, p = 0.001) and OS (OS-radiomics-signature, p = 0.038). SZLGE and the TTP-radiomics-signature additionally predicted RL. Specifically, high values for TTP-radiomics-signature and for SZLGE indicated not only earlier progression, but also a RL within the initial FET-PET active volume. Conclusion Our findings suggest that FET-PET radiomics could contribute to the prognostic assessment and selection of rGBM-patients benefiting from re-irradiation. Trial registration DRKS00000633. Registered on 8th of December in 2010

Abstract: Background: The aim of the study was to evaluate the role of different immunohistochemical and radiomics features in patients with small cell lung cancer (SCLC). Methods: Consecutive patients with histologically proven SCLC with limited (n = 47, 48%) or extensive disease (n = 51, 52%) treated with radiotherapy and chemotherapy at our department were included in the analysis. The expression of different immunohistochemical markers from the initial tissue biopsy, such as CD56, CD44, chromogranin A, synaptophysin, TTF-1, GLUT-1, Hif-1 a, PD-1, and PD-L1, and MIB-1/KI-67 as well as LDH und NSE from the initial blood sample were evaluated. H-scores were additionally generated for CD44, Hif-1a, and GLUT-1. A total of 72 computer tomography (CT) radiomics texture features from a homogenous subgroup (n = 31) of patients were correlated with the immunohistochemistry, the survival (OS), and the progression-free survival (PFS). Results: The median OS, calculated from diagnosis, was 21 months for patients with limited disease and 13 months for patients with extensive disease. The expression of synaptophysin correlated with a better OS (HR 0.546 95% CI 0.308-0.966, p = 0.03). The expression of TTF-1 (HR 0.286, 95% CI: 0.117-0.698, p = 0.006) and a lower GLUT-1 H-score (median = 50, HR: 0.511, 95% CI: 0.260-1.003, p = 0.05) correlated with a better PFS. Patients without chromogranin A expression had a higher risk for developing cerebral metastases (p = 0.02) and patients with PD 1 expression were at risk for developing metastases (p = 0.02). Our radiomics analysis did not reveal a single texture feature that correlated highly with OS or PFS. Correlation coefficients ranged between −0.48 and 0.39 for OS and between −0.46 and 0.38 for PFS. Conclusions: The role of synaptophysin should be further evaluated as synaptophysin-negative patients might profit from treatment intensification. We report an, at most, moderate correlation of radiomics features with overall and progression free survival and no correlation with the expression of different immunohistochemical markers