The aim of the current work was to present the performance evaluation procedures implemented at our department for the commissioning of a G4 CyberKnife system. This system consists of a robotic manipulator, a target-locating system and a lightweight 6-MV linac. Individual quality assurance procedures were performed for each of the CyberKnife subsystems. The system was checked for the mechanical accuracy of its robotic manipulator. The performance of the target-locating system was evaluated in terms of mechanical accuracy of both cameras' alignment and quality assurance tests of the x-ray generators and the flat-panel detectors. The traditional linac 6-MV beam characteristics and beam output parameters were also measured. Results revealed a manipulator mechanical mean accuracy of approximately 0.1 mm, with individual maximum position uncertainties less than 0.25 mm. The target-locating system mechanical accuracy was found within the acceptance limits. For the most clinically used parameters in the CyberKnife practice, e.g. 100-120 kV and 50-200 ms, kV and exposure time accuracy error were measured as less than 2%, while the precision error of the kV was determined as less than 1%. The acquired images of the ETR grid pattern revealed no geometrical distortion while the critical frequency f50 values for cameras A and B were calculated as 1.5 lp mm(-1) and 1.4 lp mm(-1), respectively. Dose placement measurements were performed in a head and neck phantom. Results revealed sub-millimeter beam delivery precision whereas the total clinical accuracy of the system was measured equal to 0.44 +/- 0.12 mm, 0.29 +/- 0.10 mm and 0.53 +/- 0.16 mm for the skull, fiducial and Xsight spine tracking methods, respectively. The results of this work certify the G4 CyberKnife SRS system capable of delivering high dose distributions with sub-millimeter accuracy and precision to intracranial and extracranial lesions. Moreover, total clinical accuracy of the investigated G4 system was found to be improved for the skull and fiducial tracking methods and was comparable for Xsight spine tracking method compared with the earlier generation of the instrument.

Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables—HPV status and tumor volume—were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.

Following literature contributions delineating the deficiencies introduced by the approximations of conventional brachytherapy dosimetry, different model-based dosimetry algorithms have been incorporated into commercial systems for (192)Ir brachytherapy treatment planning. The calculation settings of these algorithms are pre-configured according to criteria established by their developers for optimizing computation speed vs accuracy. Their clinical use is hence straightforward. A basic understanding of these algorithms and their limitations is essential, however, for commissioning; detecting differences from conventional algorithms; explaining their origin; assessing their impact; and maintaining global uniformity of clinical practice.