The 18F phantom clinical trials qualification for 18F-FDG-PET scanning adopted by GELTAMO (Grupo Español de Linfomas/Trasplante Autólogo de Médula Ósea). / Cualificación de fantomas 18F para ensayos clínicos con imagen PET/TC-18F-FDG adoptada por GELTAMO (Grupo Español de Linfomas/Trasplante Autólogo de Médula Ósea).

INTRODUCTION AND OBJECTIVES: Since different PET/CT (Positron Emission Tomography/Computed Tomography) scanners give different qualitative readings, a program for clinical trial qualification (CTQ) is mandatory to guarantee a reliable and reproducible use of PET/CT in prospective multi-centre clinical trials. Within this work we will show the results carried out in performing CTQ in Spain. MATERIALS AND METHODS: We set up, under the auspices of Grupo Español de Linfomas/Trasplante Autólogo de Médula Osea (GELTAMO), a CTQ program consisting of the acquisition and analysis of 18F uniformity and image quality phantoms for the reduction of inter-scanner variability (ISV). The ISV was estimated on background activity concentration (BAC) and sphere to background ratio (SBR) and defined as their 95% confidence level. RESULTS: Twenty-six out of 27 (96%) scanners fulfilled the CTQ requirements. The CTQ was fulfilled at the first round in 27% of the cases, while in 38%, 15% and 20%, two, three or more than three iterations, were required, respectively. The mean CTQ time was (1.8 ± 1.4) months (range: 0.3-4.6). The ISV in BAC and SBR were 20.3% and 67.7%. CONCLUSIONS: The CTQ proven to be a reliable tool to reduce ISV. This enabled to set-up clinical trials in which PET/CT was used to evaluate different clinical endpoints.

Dose prescription in SBRT for early-stage non-small cell lung cancer: are we all speaking the same language?

INTRODUCTION: Stereotactic body radiation therapy is increasingly used in the treatment of early-stage lung cancers. Guidelines provide indications regarding the constraints to the organs at risk (OARs) and the minimum coverage of the planning target volume but do not suggest optimal dose distribution. Data on dose distribution from the different published series are not comparable due to different prescription modalities and reported dose parameters. METHODS: We conducted a review of the published data on dose prescription, focusing on the role of homogeneity on local tumor control, and present suggestions on how to specify and report the prescriptions to permit comparisons between studies or between cases from different centers. CONCLUSIONS: To identify the dose-prescription modality that better correlates with oncologic outcomes, future studies should guarantee a close uniformity of dose distribution between cases and complete dose parameters reporting for treatment volumes and OARs.

The impact of time-of-flight, resolution recovery, and noise modelling in reconstruction algorithms in non-solid-state detectors PET/CT scanners: - multi-centric comparison of activity recovery in a 68Ge phantom.

The reconstruction algorithms implemented on PET/CT scanners offer gain in activity recovery of small lesions at an extent that is not full known yet. METHODS: A cylindrical phantom with warm background and hot spheres filled with a 68Ge epoxy was acquired with four non-state-solid-detectors PET/CT scanners: mCT, Ingenuity TF, Discovery 710, and IQ. Images were reconstructed switching on and off time-of-flight (TOF), point spread function (PSF) modelling, and Bayesian penalised likelihood (BPL). Images were reconstructed with the default parameters recommended by the manufacturers. The recovery coefficient (RCmax), defined as the ratio of the measured maximum activity concentration in each sphere and the actual one, and the coefficient of variation (CoVBAC) defined as the ratio of the standard deviation and the average of background activity concentration were measured. RESULTS: While with IR alone, complete recovery of the activity concentration is achieved down to the 22 mm diameter's sphere, with TOF, TOF + PSF and BPL it is achieved down to the 17 mm diameter one. At smaller dimensions, the difference among the various studied reconstruction algorithms is substantial for the 13- and 17-mm diameters' spheres for all scanners and for all reconstructions with a considerable gain in RCmax when PSF and BPL are used. At 10 mm diameter's sphere the difference among the algorithms is significantly reduced, except for BPL which still guarantees a gain in RCmax.

Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.

OBJECTIVE: To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection with the analysis of radiomics features. METHOD: The investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules were identified by visual analysis and then classified using the diameter and the radiological aspect of the nodule following lung-RADS. Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using logistic regression on a subset of variables selected with backward feature selection and using two machine learning: a Random Forest and a neural network with the whole subset of variables. The methods were applied to a train set and validated on a test set where diagnostic accuracy metrics were calculated. RESULTS: Binary visual analysis had a good sensitivity (0.95) but a low PPV (0.14). Lung-RADS classification increased the PPV (0.19) but with an unacceptable low sensitivity (0.65). Logistic regression showed a mildly increased PPV (0.29) but a lower sensitivity (0.20). Random Forest demonstrated a moderate PPV (0.40) but with a low sensitivity (0.30). Neural network demonstrated to be the best predictor with a high PPV (0.95) and a high sensitivity (0.90). CONCLUSIONS: The neural network demonstrated the best PPV. The use of visual analysis along with neural network could help radiologists to reduce the number of false positive in DTS. KEY POINTS: • We investigated several approaches to enhance the positive predictive value of chest digital tomosynthesis in the lung cancer detection. • Neural network demonstrated to be the best predictor with a nearly perfect PPV. • Neural network could help radiologists to reduce the number of false positive in DTS.