Overall, excellent agreement was observed in TrueBeam commissioning data. This set of multi-institutional data can provide comparison data to others embarking on TrueBeam commissioning, ultimately improving the safety and quality of beam commissioning.

No abstract

Contouring of targets and normal tissues is one of the largest sources of variability in radiation therapy treatment plans. Contours thus require a time intensive and error-prone quality assurance (QA) evaluation, limitations which also impair the facilitation of adaptive radiotherapy (ART). Here, an automated system for contour QA is developed using historical data (the 'knowledge base'). A pilot study was performed with a knowledge base derived from 9 contours each from 29 head-and-neck treatment plans. Size, shape, relative position, and other clinically-relevant metrics and heuristically derived rules are determined. Metrics are extracted from input patient data and compared against rules determined from the knowledge base; a computer-learning component allows metrics to evolve with more input data, including patient specific data for ART. Nine additional plans containing 42 unique contouring errors were analyzed. 40/42 errors were detected as were 9 false positives. The results of this study imply knowledge-based contour QA could potentially enhance the safety and effectiveness of RT treatment plans as well as increase the efficiency of the treatment planning process, reducing labor and the cost of therapy for patients.

Hyperthermia therapy (HT) raises tissue temperature to 40–45°C for up to 60 minutes. Hyperthermia is one of the most potent sensitizers of radiation therapy (RT). Ultrasound-mediated HT for radiosensitization has been used clinically since the 1960s. Recently, magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU), which has been approved by the United States Food and Drug Administration for thermal ablation therapy, has been adapted for HT. With emerging clinical trials using MRgHIFU HT for radiosensitization, there is a pressing need to review the ultrasound HT technology. The objective of this review is to overview existing HT technology, summarize available ultrasound HT devices, evaluate clinical studies combining ultrasound HT with RT, and discuss challenges and future directions.

We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the "detection task" and the "classification task." Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation.