BackgroundThe accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT.MethodsFive clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA).ResultsFor all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency.ConclusionsImprovements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.

A semiautomated system for radiotherapy treatment plan quality control (QC), named AutoLock, is presented. AutoLock is designed to augment treatment plan QC by automatically checking aspects of treatment plans that are well suited to computational evaluation, whilst summarizing more subjective aspects in the form of a checklist. The treatment plan must pass all automated checks and all checklist items must be acknowledged by the planner as correct before the plan is finalized. Thus AutoLock uniquely integrates automated treatment plan QC, an electronic checklist, and plan finalization. In addition to reducing the potential for the propagation of errors, the integration of AutoLock into the plan finalization workflow has improved efficiency at our center. Detailed audit data are presented, demonstrating that the treatment plan QC rejection rate fell by around a third following the clinical introduction of AutoLock.PACS number: 87.55.Qr

Kilovoltage cone-beam CT (kV CBCT) can be acquired during the delivery of volumetric modulated arc therapy (VMAT), in order to obtain an image of the patient during treatment. However, the quality of such CBCTs is degraded by megavoltage (MV) scatter from the treatment beam onto the imaging panel. The objective of this paper is to introduce a novel MV scatter correction method for simultaneous CBCT during VMAT, and to investigate its effectiveness when compared to other techniques. The correction requires the acquisition of a separate set of images taken during VMAT delivery, while the kV beam is off. These images--which contain only the MV scatter contribution on the imaging panel--are then used to correct the corresponding kV/MV projections. To test this method, CBCTs were taken of an image quality phantom during VMAT delivery and measurements of contrast to noise ratio were made. Additionally, the correction was applied to the datasets of three VMAT prostate patients, who also received simultaneous CBCTs. The clinical image quality was assessed using a validated scoring system, comparing standard CBCTs to the uncorrected simultaneous CBCTs and a variety of correction methods. Results show that the correction is able to recover some of the low and high-contrast signal to noise ratio lost due to MV scatter. From the patient study, the corrected CBCT scored significantly higher than the uncorrected images in terms of the ability to identify the boundary between the prostate and surrounding soft tissue. In summary, a simple MV scatter correction method has been developed and, using both phantom and patient data, is shown to improve the image quality of simultaneous CBCTs taken during VMAT delivery.