Purpose Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI‐only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle‐consistent generative adversarial networks (cycle GAN), a deep‐learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. Methods and materials The cycle GAN‐based model was developed to generate sCT images in a patch‐based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one‐to‐one mapping. Dense block‐based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. Results Leave‐one‐out cross‐validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. Conclusion We developed and validated a novel learning‐based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high‐quality sCT in minutes. The proposed method offers strong potential for supporting near real‐time MRI‐only treatment planning in the brain and pelvis.

The aim of this study was to compare outcomes of postoperative whole brain radiation therapy (WBRT) to stereotactic radiosurgery (SRS) alone in patients with resected brain metastases (BM). We reviewed records of patients who underwent surgical resection of BM followed by WBRT or SRS alone between 2003 and 2013. Local control (LC) of the treated resected cavity, distant brain control (DBC), leptomeningeal disease (LMD), overall survival (OS), and radiographic leukoencephalopathy rates were estimated by the Kaplan-Meier method. One-hundred thirty-two patients underwent surgical resection for 141 intracranial metastases: 36 (27 %) patients received adjuvant WBRT and 96 (73 %) received SRS alone to the resection cavity. One-year OS (56 vs. 55 %, p = 0.64) and LC (83 vs. 74 %, p = 0.31) were similar between patients receiving WBRT and SRS. After controlling for number of BM, WBRT was associated with higher 1-year DBC compared with SRS (70 vs. 48 %, p = 0.03); single metastasis and WBRT were the only significant predictors for reduced distant brain recurrence in multi-variate analysis. Freedom from LMD was higher with WBRT at 18 months (87 vs. 69 %, p = 0.045), while incidence of radiographic leukoencephalopathy was higher with WBRT at 12 months (47 vs. 7 %, p = 0.001). One-year freedom from WBRT in the SRS alone group was 86 %. Compared with WBRT for patients with resected BM, SRS alone demonstrated similar LC, higher rates of LMD and inferior DBC, after controlling for the number of BM. However, OS was similar between groups. The results of ongoing clinical trials are needed to confirm these findings.

Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. Our machine-learning-based method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality, while its dose calculation accuracy remains an open question. In this study, we aim to investigate the accuracy of dose calculation in brain frameless stereotactic radiosurgery (SRS) using pseudo CT images which are generated from MRI images using the machine learning-based method developed by our group. We retrospectively investigated a total of 19 treatment plans from 14 patients, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. Clinically-relevant DVH metrics and gamma analysis were extracted from both ground truth and pseudo CT results for comparison and evaluation. The side-by-side comparisons on image quality and dose distributions demonstrated very good agreement of image contrast and calculated dose between pseudo CT and original CT. The average differences in Dose-volume histogram (DVH) metrics for Planning target volume (PTVs) were less than 0.6%, and no differences in those for organs at risk at a significance level of 0.05. The average pass rate of gamma analysis was 99%. These quantitative results strongly indicate that the pseudo CT images created from MRI images using our proposed machine learning method are accurate enough to replace current CT simulation images for dose calculation in brain SRS treatment. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning.

Frameless radiosurgery is an attractive alternative to the framed procedure if it can be performed with comparable precision in a reasonable time frame. Here, we present a positioning approach for frameless radiosurgery based on in‐room volumetric imaging coupled with an advanced six‐degrees‐of‐freedom (6 DOF) image registration technique which avoids use of a bite block. Patient motion is restricted with a custom thermoplastic mask. Accurate positioning is achieved by registering a cone‐beam CT to the planning CT scan and applying all translational and rotational shifts using a custom couch mount. System accuracy was initially verified on an anthropomorphic phantom. Isocenters of delineated targets in the phantom were computed and aligned by our system with an average accuracy of 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively. The accuracy in the rotational directions was 0.1°, 0.2°, and 0.1° in the pitch, roll, and yaw, respectively. An additional test was performed using the phantom in which known shifts were introduced. Misalignments up to 10 mm and 3° in all directions/rotations were introduced in our phantom and recovered to an ideal alignment within 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively, and within 0.3° in any rotational axis. These values are less than couch motion precision. Our first 28 patients with 38 targets treated over 63 fractions are analyzed in the patient positioning phase of the study. Mean error in the shifts predicted by the system were less than 0.5 mm in any translational direction and less than 0.3° in any rotation, as assessed by a confirmation CBCT scan. We conclude that accurate and efficient frameless radiosurgery positioning is achievable without the need for a bite block by using our 6 DOF registration method. This system is inexpensive compared to a couch‐based 6 DOF system, improves patient comfort compared to systems that utilize a bite block, and is ideal for the treatment of pediatric patients with or without general anesthesia, as well as of patients with dental issues. From this study, it is clear that only adjusting for 4 DOF may, in some cases, lead to significant compromise in PTV coverage. Since performing the additional match with 6 DOF in our registration system only adds a relatively short amount of time to the overall process, we advocate making the precise match in all cases.PACS number: 87.55.tm; 87.55.Qr; 87.57.nj

Introduction Neutrophil-to-lymphocyte ratio (NLR) is a surrogate for systemic inflammatory response and its elevation has been shown to be a poor prognostic factor in various malignancies. Stereotactic radiosurgery (SRS) can induce a leukocytepredominant inflammatory response. This study investigates the prognostic impact of post-SRS NLR in patients with brain métastasés (BM). Methods BM patients treated with SRS from 2003 to 2015 were retrospectively identified. NLR was calculated from the most recent full blood counts post-SRS. Overall survival (OS) and intracranial outcomes were calculated using the Kaplan-Meier method and cumulative incidence with competing risk for death, respectively. Results 188 patients with 328 BM treated with SRS had calculable post-treatment NLR values. Of these, 51 (27.1%) had a NLR >6. The overall median imaging follow-up was 13.2 (14.0 vs. 8.7 for NLR ≤ 6.0 vs. >6.0) months. Baseline patient and treatment characteristics were well balanced, except for lower rate of ECOG performance status 0 in the NLR > 6 cohort (33.3 vs. 44.2%, p = 0.026). NLR >6 was associated with worse 1- and 2-year OS: 59.9 vs. 72.9% and 24.6 vs. 43.8%, (p = 0.028). On multivariable analysis, NLR > 6 (HR: 1.53; 95% CI 1.03–2.26, p = 0.036) and presence of extracranial metastases (HR: 1.90; 95% CI 1.30–2.78; p < 0.001) were significant predictors for worse OS. No association was seen with NLR and intracranial outcomes. Conclusion Post-treatment NLR, a potential marker for post-SRS inflammatory response, is inversely associated with OS in patients with BM. If prospectively validated, NLR is a simple, systemic marker that can be easily used to guide subsequent management.