35 research outputs found

    Conventions for composition conversion

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    SBRT for early-stage glottic larynx cancer-Initial clinical outcomes from a phase I clinical trial.

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    To confirm safety and feasibility of hypofractionated SBRT for early-stage glottic laryngeal cancer.Twenty consecutive patients with cTis-T2N0M0 carcinoma of glottic larynx were enrolled. Patients entered dose-fractionation cohorts of incrementally shorter bio-equivalent schedules starting with 50 Gy in 15 fractions (fx), followed by 45 Gy/10 fx and, finally, 42.5 Gy/5 fx. Maximum combined CTV-PTV expansion was limited to 5 mm. Patients were treated on a Model G5 Cyberknife (Accuray, Sunnyvale, CA).Median follow-up is 13.4 months (range: 5.6-24.6 months), with 12 patients followed for at least one year. Maximum acute toxicity consisted of grade 2 hoarseness and dysphagia. Maximum chronic toxicity was seen in one patient treated with 45 Gy/10 fx who continued to smoke >1 pack/day and ultimately required protective tracheostomy. At 1-year follow-up, estimated local disease free survival for the full cohort was 82%. Overall survival is 100% at last follow-up.We were able to reduce equipotent total fractions of SBRT from 15 to 5 without exceeding protocol-defined acute/subacute toxicity limits. With limited follow-up, disease control appears comparable to standard treatment. We continue to enroll to the 42.5 Gy/5 fx cohort and follow patients for late toxicity.ClinicalTrials.gov NCT01984502

    Considerations of target surface area and the risk of radiosurgical toxicity.

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    OBJECTIVE:The goal of this study was to explore conceptual benefits of characterizing delineated target volumes based on surface area and to utilize the concept for assessing risk of therapeutic toxicity in radiosurgery. METHODS AND MATERIALS:Four computer-generated targets, a sphere, a cylinder, an ellipsoid and a box, were designed for two distinct scenarios. In the first scenario, all targets had identical volumes, and in the second one, all targets had identical surface areas. High quality stereotactic radiosurgery plans with at least 95% target coverage and selectivity were created for each target in both scenarios. Normal brain volumes V12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, were computed and analyzed. Additionally, V12Gy and V14Gy volumes and values for seven prospective toxicity variables were recorded for 100 meningioma patients after Gamma Knife radiosurgery. Multivariable stepwise linear regression and best subset linear regression analyses were performed in two statistical software packages, SAS/STAT and R, respectively. RESULTS:In a phantom study, for the constant volume targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the lowest for the spherical target as an expected corollary of the isoperimetric inequality. For the constant surface area targets, a conventional wisdom is confirmed, as the target volume increases the corresponding volumes V12Gy, V14Gy and V16Gy also increase. In the 100-meningioma patient cohort, the best univariate model featured tumor surface area as the most significantly associated variable with both V12Gy and V14Gy volumes, corresponding to the adjusted R2 values of 0.82 and 0.77, respectively. Two statistical methods converged to matching multivariable models. CONCLUSIONS:In a univariate model, target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. In complex multivariate models, target surface area is an independent variable for modeling radiosurgical normal tissue toxicity risk

    A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

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    Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases
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