Citations

Background Predicting recanalization success for patients undergoing endovascular treatment for acute ischemic stroke is of significant interest. Studies have previously correlated the success of recanalization with the density of the clot. We evaluated clot density and its relationship to revascularization success and stroke etiology. Methods We conducted a retrospective review of 118 patients undergoing intra-arterial therapy for acute ischemic stroke. Mean and maximum thrombus density was measured by drawing a circular region of interest on an axial slice of a non-contrast computed tomography scan. T-tests were used to compare clot density to recanalization success or to stroke etiology, namely large artery atherosclerosis and cardioembolism. Recanalization success was compared in four device groups: aspiration, stent retriever, aspiration and stent retriever, and all other. Results There was no significant difference in the mean clot density in patients with successful ( n = 80) versus unsuccessful recanalization ( n = 38, 50.1 ± 7.4 Hounsfield unit (HU) vs. 53 ± 12.7 HU; P = 0.17). Comparing the large artery thromboembolism ( n = 35) to the cardioembolic etiology group ( n = 56), there was no significant difference in mean clot density (51.5 ± 7.7 HU vs. 49.7 ± 8.5 HU; P = 0.31). A subgroup analysis of middle cerebral artery occlusions ( n = 65) showed similar, non-statistically significant differences between groups. There was no difference in the rate of recanalization success in patients with a mean clot density greater than 50 HU or less than 50 HU in each of the four device groups. Conclusions There was no relationship between clot density and revascularization success or stroke etiology in our study. More research is needed to determine if clot density can predict recanalization rates or indicate etiology.

The purpose of the present study was to compare the impact of pulmonary function, body habitus, and stereotactic body radiation therapy (SBRT) immobilization on setup and reproducibility for upper lung tumor. From 2008 through 2011, our institution's prospective SBRT database was searched for patients with upper lung tumors. Two SBRT immobilization strategies were used: full‐length BodyFIX and thermoplastic S‐frame. At simulation, free‐breathing, four‐dimensional computed tomography was performed. For each treatment, patients were set up to isocenter with in‐room lasers and skin tattoos. Shifts from initial and subsequent couch positions with cone‐beam computed tomography (CBCT) were analyzed. Accounting for setup uncertainties, institutional tolerance of CBCT‐based shifts for treatment was 2, 2, and 4 mm in left–right, anterior–posterior, and cranial–caudal directions, respectively; shifts exceeding these limits required reimaging. Each patient's pretreatment pulmonary function test was recorded. A multistep, multivariate linear regression model was performed to elucidate intervariable dependency for three‐dimensional calculated couch shift parameters. BodyFIX was applied to 76 tumors and S‐frame to 17 tumors. Of these tumors, 41 were non–small cell lung cancer and 15 were metastatic from other sites. Lesions measured , 1.1 to 2 (50%), 2.1 to 3 (25%), and cm. Errors from first shifts of first fractions were significantly less with S‐frame than BodyFIX (). No difference in local control (LC) was found between S‐frame and BodyFIX (); two‐year LC rate was 94%. Multivariate modeling confirmed that the ratio of forced expiratory volume in the first second of expiration to forced vital capacity, body habitus, and the immobilization device significantly impacted couch shift errors. For upper lung tumors, initial setup was more consistent with S‐frame than BodyFIX, resulting in fewer CBCT scans. Patients with obese habitus and poor lung function had more SBRT setup uncertainty; however, outcome and probability for LC remained excellent.PACS number: 89.20.‐a

The purpose of this study is to apply the principles of statistical process control (SPC) in the context of patient specific intensity‐modulated radiation therapy (IMRT) QA to set clinic‐specific action limits and evaluate the impact of changes to the multileaf collimator (MLC) calibrations on IMRT QA results. Ten months of IMRT QA data with 247 patient QAs collected on three beam‐matched linacs were retrospectively analyzed with a focus on the gamma pass rate (GPR) and the average ratio between the measured and planned doses. Initial control charts and action limits were calculated. Based on this data, changes were made to the leaf gap parameter for the MLCs to improve the consistency between linacs. This leaf gap parameter is tested monthly using a MLC sweep test. A follow‐up dataset with 424 unique QAs were used to evaluate the impact of the leaf gap parameter change. The initial data average GPR was 98.6% with an SPC action limit of 93.7%. The average ratio of doses was 1.003, with an upper action limit of 1.017 and a lower action limit of 0.989. The sweep test results for the linacs were , and from nominal. After the adjustment of the leaf gap parameter, all sweep test results were within 0.4% of nominal. Subsequently, the average GPR was 99.4% with an SPC action limit of 97.3%. The average ratio of doses was 0.997 with an upper action limit of 1.011 and a lower action limit of 0.981. Applying the principles of SPC to IMRT QA allowed small differences between closely matched linacs to be identified and reduced. Ongoing analysis will monitor the process and be used to refine the clinical action limits for IMRT QA.PACS number: 87.55.Qr

The objective of this study was to evaluate and understand the systematic error between the planned three‐dimensional (3D) dose and the delivered dose to patient in scanning beam proton therapy for lung tumors. Single‐field and multifield optimized scanning beam proton therapy plans were generated for ten patients with stage II‐III lung cancer with a mix of tumor motion and size. 3D doses in CT datasets for different respiratory phases and the time‐weighted average CT, as well as the four‐dimensional (4D) doses were computed for both plans. The 3D and 4D dose differences for the targets and different organs at risk were compared using dose‐volume histogram (DVH) and voxel‐based techniques, and correlated with the extent of tumor motion. The gross tumor volume (GTV) dose was maintained in all 3D and 4D doses, using the internal GTV override technique. The DVH and voxel‐based techniques are highly correlated. The mean dose error and the standard deviation of dose error for all target volumes were both less than 1.5% for all but one patient. However, the point dose difference between the 3D and 4D doses was up to 6% for the GTV and greater than 10% for the clinical and planning target volumes. Changes in the 4D and 3D doses were not correlated with tumor motion. The planning technique (single‐field or multifield optimized) did not affect the observed systematic error. In conclusion, the dose error in 3D dose calculation varies from patient to patient and does not correlate with lung tumor motion. Therefore, patient‐specific evaluation of the 4D dose is important for scanning beam proton therapy for lung tumors.PACS number: 87.55.D

The purpose of this study was to develop a simplified methodology that will produce Monte Carlo (MC) dose distribution for proton therapy which can be used as a clinical aid in determining the adequacy of proton plans produced from the treatment planning system (TPS). The Geant4 Monte Carlo toolkit was used for all simulations. The geometry of the double scatter nozzle in the simulation was a simplification of the treatment nozzle. The proton source was modeled as discrete energy layers, each with a unique energy distribution and weighting factor. The simplified MC system was designed to give the same dose distribution as the measured data used to commission the TPS. After the simplified MC system was finalized, a series of verification comparisons were made between it, measurements, and the clinically used TPS. Comparisons included the lateral profile of a stair‐shaped compensator that simulated a sharp lateral heterogeneity and depth‐dose measurements through heterogeneous materials. The simplified MC system matched measurements to within 2% or 2 mm for all commissioning data under investigation; moreover, the distal edge and lateral penumbra was within 1 mm of the measurements. The simplified MC system was able to better reproduce the measured profiles for a stair‐shaped compensator than the TPS. Both MC and TPS matched the measured depth dose through heterogeneous materials to within 2% or 2 mm. The simplified MC system was straightforward to implement, and produced accurate results when compared to measurements. Therefore, it holds promise as a clinically useful methodology to validate the relative dose distribution of patient treatment plans produced by the treatment planning systems.PACS number: 87.55.K‐, 87.55.ne

Because treatment planning systems (TPSs) generally do not provide monitor units (MUs) for double‐scattered proton plans, models to predict MUs as a function of the range and the nominal modulation width requested of the beam delivery system, such as the one developed by the MGH group, have been proposed. For a given nominal modulation width, however, the measured modulation width depends on the accuracy of the vendor's calibration process and may differ from this nominal value, and also from one beamline to the next. Although such a difference can be replicated in our TPS, the output dependence on range and modulation width for each beam option or suboption has to be modeled separately for each beamline in order to achieve maximal 3% inaccuracy. As a consequence, the MGH output model may not be directly transferable. This work, therefore, serves to extend the model to more general clinic situations. In this paper, a parameterized linear‐quadratic transformation is introduced to convert the nominal modulation width to the measured modulation width for each beam option or suboption on a per‐beamline basis. Fit parameters are derived for each beamline from measurements of 60 reference beams spanning the minimum and maximum ranges, and modulation widths from 2 cm to full range per option or suboption. Using the modeled modulation width, we extract the MGH parameters for the output dependence on range and modulation width. Our method has been tested with 1784 patient‐specific fields delivered across three different beamlines at our facility. For these fields, all measured outputs fall within 3%, and 64.4% fall within 1%, of our model. Using a parameterized linear‐quadratic modulation width, MU calculation models can be established on a per‐beamline basis for each double scattering beam option or suboption.PACS number: 87.53.Qc

The purpose of this study is to investigate changes in lung tumor internal target volume during stereotactic body radiotherapy treatment (SBRT) using magnetic resonance imaging (MRI). Ten lung cancer patients (13 tumors) undergoing SBRT (48 Gy over four consecutive days) were evaluated. Each patient underwent three lung MRI evaluations: before SBRT (MRI‐1), after fraction 3 of SBRT (MRI‐3), and three months after completion of SBRT (MRI‐3m). Each MRI consisted of T1‐weighted images in axial plane through the entire lung. A cone‐beam CT (CBCT) was taken before each fraction. On MRI and CBCT taken before fractions 1 and 3, gross tumor volume (GTV) was contoured and differences between the two volumes were compared. Median tumor size on CBCT before fractions 1 (CBCT‐1) and 3 (CBCT‐3) was 8.68 and 11.10 cm3, respectively. In 12 tumors, the GTV was larger on CBCT‐3 compared to CBCT‐1 (median enlargement, 1.56 cm3). Median tumor size on MRI‐1, MRI‐3, and MRI‐3m was 7.91, 11.60, and 3.33 cm3, respectively. In all patients, the GTV was larger on MRI‐3 compared to MRI‐1 (median enlargement, 1.54 cm3). In all patients, GTV was smaller on MRI‐3m compared to MRI‐1 (median shrinkage, 5.44 cm3). On CBCT and MRI, all patients showed enlargement of the GTV during the treatment week of SBRT, except for one patient who showed minimal shrinkage (0.86 cm3). Changes in tumor volume are unpredictable; therefore, motion and breathing must be taken into account during treatment planning, and image‐guided methods should be used, when treating with large fraction sizes.PACS number: 87.53.Ly