Design study of a high power in-pile nuclear thermionic space powerplant final report

Thermionic nuclear spacecraft power plan

Historical cohort study examining comparative effectiveness of albuterol inhalers with and without integrated dose counter for patients with asthma or chronic obstructive pulmonary disease

This study was supported financially by an unrestricted grant from Teva Pharmaceuticals, Frazer, PA, USA. The authors thank Jenny Fanstone of Fanstone Medical Communications Ltd., UK, and Elizabeth V Hillyer for medical writing support, funded by Research in Real-Life. We acknowledge with gratitude Dr Ruchir Parikh for his review of and contributions to the manuscript.Peer reviewedPublisher PD

Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network

The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image assessment parameters through network-wide cooperative projects. To more effectively use the cooperative power of the network in conducting computational challenges in benchmarking of tools and methods and collaborative projects in analytical assessment of imaging technologies, the QIN Challenge Task Force has developed policies and procedures to enhance the value of these activities by developing guidelines and leveraging NCI resources to help their administration and manage dissemination of results. Challenges and Collaborative Projects (CCPs) are further divided into technical and clinical CCPs. As the first NCI network to engage in CCPs, we anticipate a variety of CCPs to be conducted by QIN teams in the coming years. These will be aimed to benchmark advanced software tools for clinical decision support, explore new imaging biomarkers for therapeutic assessment, and establish consensus on a range of methods and protocols in support of the use of quantitative imaging to predict and assess response to cancer therapy

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

PurposeThe study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).MethodsThe DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.ResultsThere was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and ‘dead’ group in their Kaplan-Meyer curves (p = 0.019).ConclusionDeep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.SummaryWhile current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients

Fractionated stereotactic radiotherapy for skull base tumors: analysis of treatment accuracy using a stereotactic mask fixation system

Background: To assess the accuracy of fractionated stereotactic radiotherapy (FSRT) using a stereotactic mask fixation system. Patients and Methods: Sixteen patients treated with FSRT were involved in the study. A commercial stereotactic mask fixation system (BrainLAB AG) was used for patient immobilization. Serial CT scans obtained before and during FSRT were used to assess the accuracy of patient immobilization by comparing the isocenter position. Daily portal imaging were acquired to establish day to day patient position variation. Displacement errors along the different directions were calculated as combination of systematic and random errors. Results: The mean isocenter displacements based on localization and verification CT imaging were 0.1 mm (SD 0.3 mm) in the lateral direction, 0.1 mm (SD 0.4 mm) in the anteroposterior, and 0.3 mm (SD 0.4 mm) in craniocaudal direction. The mean 3D displacement was 0.5 mm (SD 0.4 mm), being maximum 1.4 mm. No significant differences were found during the treatment (P = 0.4). The overall isocenter displacement as calculated by 456 anterior and lateral portal images were 0.3 mm (SD 0.9 mm) in the mediolateral direction, -0.2 mm (SD 1 mm) in the anteroposterior direction, and 0.2 mm (SD 1.1 mm) in the craniocaudal direction. The largest displacement of 2.7 mm was seen in the cranio-caudal direction, with 95% of displacements < 2 mm in any direction. Conclusions: The results indicate that the setup error of the presented mask system evaluated by CT verification scans and portal imaging are minimal. Reproducibility of the isocenter position is in the best range of positioning reproducibility reported for other stereotactic systems

Frameless linac-based stereotactic radiosurgery (SRS) for brain metastases: analysis of patient repositioning using a mask fixation system and clinical outcomes

<p>Abstract</p> <p>Purpose</p> <p>To assess the accuracy of patient repositioning and clinical outcomes of frameless stereotactic radiosurgery (SRS) for brain metastases using a stereotactic mask fixation system.</p> <p>Patients and Methods</p> <p>One hundred two patients treated consecutively with frameless SRS as primary treatment at University of Rome Sapienza Sant'Andrea Hospital between October 2008 and April 2010 and followed prospectively were involved in the study. A commercial stereotactic mask fixation system (BrainLab) was used for patient immobilization. A computerized tomography (CT) scan obtained immediately before SRS was used to evaluate the accuracy of patient repositioning in the mask by comparing the isocenter position to the isocenter position established in the planning CT. Deviations of isocenter coordinates in each direction and 3D displacement were calculated. Overall survival, brain control, and local control were estimated using the Kaplan-Meier method calculated from the time of SRS.</p> <p>Results</p> <p>The mean measured isocenter displacements were 0.12 mm (SD 0.35 mm) in the lateral direction, 0.2 mm (SD 0.4 mm) in the anteroposterior, and 0.4 mm (SD 0.6 mm) in craniocaudal direction. The maximum displacement of 2.1 mm was seen in craniocaudal direction. The mean 3D displacement was 0.5 mm (SD 0.7 mm), being maximum 2.9 mm. The median survival was 15.5 months, and 1-year and 2-year survival rates were 58% and 24%, respectively. Nine patients recurred locally after SRS, with 1-year and 2-year local control rates of 91% and 82%, respectively. Stable extracranial disease (P = 0.001) and KPS > 70 (P = 0.01) were independent predictors of survival.</p> <p>Conclusions</p> <p>Frameless SRS is an effective treatment in the management of patients with brain metastases. The presented non-invasive mask-based fixation stereotactic system is associated with a high degree of patient repositioning accuracy; however, a careful evaluation is essential since occasional errors up to 3 mm may occur.</p