Is the CyberKnife radiosurgery system effective and safe for patients? An umbrella review of the evidence

Technical note: MR image-based synthesis CT for CyberKnife robotic stereotactic radiosurgery

The purpose of this study is to investigate whether deep learning-based sCT images enable accurate dose calculation in CK robotic stereotactic radiosurgery. A U-net convolutional neural network was trained using 2446 MR-CT pairs and used it to translate 551 MR images to sCT images for testing. The sCT of CK patient was encapsulated into a quality assurance (QA) validation phantom for dose verification. The CT value difference between CT and sCT was evaluated using mean absolute error (MAE) and the statistical significance of dose differences between CT and sCT was tested using the Wilcoxon signed rank test. For all CK patients, the MAE value of the whole brain region did not exceed 25 HU. The percentage dose difference between CT and sCT was less than ±0.4% on GTV (D2(Gy), -0.29%, D95(Gy), -0.09%), PTV (D2(Gy), -0.25%, D95(Gy), -0.10%), and brainstem (max dose(Gy), 0.31%). The percentage dose difference between CT and sCT for most regions of interest (ROIs) was no more than ±0.04%. This study extended MR-based sCT prediction to CK robotic stereotactic radiosurgery, expanding the application scenarios of MR-only radiation therapy. The results demonstrated the remarkable accuracy of dose calculation on sCT for patients treated with CK robotic stereotactic radiosurgery.

Longitudinal analysis of T2 relaxation time variations following radiotherapy for prostate cancer

Aim of this paper is to evaluate short and long-term changes in T 2 relaxation times after radiotherapy in patients with low and intermediate risk localized prostate cancer. A total of 24 patients were selected for this retrospective study. Each participant underwent 1.5T magnetic resonance imaging on seven separate occasions: initially after the implantation of gold fiducials, the required step for Cyberknife therapy guidance, followed by MRI scans two weeks post-therapy and monthly thereafter. As part of each MRI scan, the prostate region was manually delineated, and the T 2 relaxation times were calculated for quantitative analysis. The T 2 relaxation times between individual follow-ups were analyzed using Repeated Measures Analysis of Variance that revealed a significant difference across all measurements (F (6, 120) = 0.611, p << 0.001). A Bonferroni post hoc test revealed significant differences in median T 2 values between the baseline and subsequent measurements, particularly between pre-therapy (M 0 ) and two weeks post-therapy (M 1 ), as well as during the monthly interval checks (M 2 - M 6 ). Some cases showed a delayed decrease in relaxation times, indicating the prolonged effects of therapy. The changes in T 2 values during the course of radiotherapy can help in monitoring radiotherapy response in unconfirmed patients, quantifying the scarring process, and recognizing the therapy failure.

Initial Experience With 6D Skull Tracking and Intrafractional Motion Monitoring in the United Arab Emirates' First CyberKnife® Radiosurgery Center

Introduction The introduction of the CyberKnife® system has marked a significant advancement in the field of radiosurgery, offering unparalleled precision in targeting and treating cranial and extracranial lesions. This paper details the first experience from the United Arab Emirates in implementing 6D skull tracking and intrafractional motion monitoring in CyberKnife® radiosurgery. The study aims to evaluate the system's efficacy in tracking and adjusting patient movement during treatment, enhancing treatment accuracy and patient safety. Methods and materials This retrospective study analyzed 732 images from six patients treated at the UAE's first CyberKnife® center. Patients were divided into two groups based on their treatment regimens: Patients 1 to 4 (P1 to P4) received multifractionated stereotactic radiotherapy, while Patients 5 and 6 (P5 and P6) underwent single-fraction stereotactic radiosurgery (SRS). The movements recorded included supero-inferior, lateral, antero-posterior, roll, pitch, and yaw. Statistical tools were employed to interpret the data, including heat maps, box-and-whisker plots, and correlation analysis. Results The study's results indicate varied patterns of intrafractional movement across the different axes and between the two treatment groups. Multifractionated therapy patients exhibited a specific range and frequency of movements compared to those undergoing single-fraction treatment. The most significant movements were observed in the supero-inferior and lateral axes. Discussion The findings suggest that the CyberKnife® system's real-time tracking and adaptive capabilities are crucial in managing patient movements, especially in prolonged treatment sessions. The differences in movement patterns between multifractionated and single-fraction treatments underscore the need for tailored approaches in intrafractional motion monitoring. Conclusion The initial experience of the UAE's first CyberKnife® center demonstrates the system's effectiveness in addressing intrafractional movements, enhancing the precision and safety of radiosurgery treatments. This study contributes valuable insights into optimizing treatment protocols and underscores the importance of continuous monitoring and adaptive strategies in advanced radiosurgery.

Three-dimensional dose prediction based on deep convolutional neural networks for brain cancer in CyberKnife: accurate beam modelling of homogeneous tissue

Objectives

Accurate beam modelling is essential for dose calculation in stereotactic radiation therapy (SRT), such as CyberKnife treatment. However, the present deep learning methods only involve patient anatomical images and delineated masks for training. These studies generally focus on traditional intensity-modulated radiation therapy (RT) plans. Nevertheless, this paper aims to develop a deep CNN-based method for CyberKnife plan dose prediction about brain cancer patients. It utilized modelled beam information, target delineation, and patient anatomical information.

Methods

This study proposes a method that adds beam information to predict the dose distribution of CyberKnife in brain cases. A retrospective dataset of 88 brain and abdominal cancer patients treated with the Ray-tracing algorithm was performed. The datasets include patients' anatomical information (planning CT), binary masks for organs at risk (OARs) and targets, and clinical plans (containing beam information). The datasets were randomly split into 68, 6, and 14 brain cases for training, validation, and testing, respectively.

Results

Our proposed method performs well in SRT dose prediction. First, for the gamma passing rates in brain cancer cases, with the 2 mm/2% criteria, we got 96.7% ± 2.9% for the body, 98.3% ± 3.0% for the planning target volume, and 100.0% ± 0.0% for the OARs with small volumes referring to the clinical plan dose. Secondly, the model predictions matched the clinical plan's dose-volume histograms reasonably well for those cases. The differences in key metrics at the target area were generally below 1.0 Gy (approximately a 3% difference relative to the prescription dose).

Conclusions

The preliminary results for selected 14 brain cancer cases suggest that accurate 3-dimensional dose prediction for brain cancer in CyberKnife can be accomplished based on accurate beam modelling for homogeneous tumour tissue. More patients and other cancer sites are needed in a further study to validate the proposed method fully.

Advances in knowledge

With accurate beam modelling, the deep learning model can quickly generate the dose distribution for CyberKnife cases. This method accelerates the RT planning process, significantly improves its operational efficiency, and optimizes it.

Combination of radiotherapy and targeted therapy for HER2-positive breast cancer brain metastases

Radiotherapy and targeted therapy are essential treatments for patients with brain metastases from human epidermal growth factor receptor 2 (HER2)-positive breast cancer. However, the combination of radiotherapy and targeted therapy still needs to be investigated, and neurotoxicity induced by radiotherapy for brain metastases has also become an important issue of clinical concern. It remained unclear how to achieve the balance of efficacy and toxicity with the application of new radiotherapy techniques and new targeted therapy drugs. This article reviews the benefits and potential risk of combining radiotherapy and targeted therapy for HER2-positive breast cancer with brain metastases.