Purpose: To compare two coverage-based planning (CP) techniques with standard fixed marginbased planning (FM), considering the dosimetric impact of interfraction deformable organ motion exclusively for high-risk prostate treatments. Methods: Nineteen prostate cancer patients with 8-13 prostate CT images of each patient were used to model patient-specific interfraction deformable organ changes. The model was based on the principal component analysis (PCA) method and was used to predict the patient geometries for virtual treatment course simulation. For each patient, an IMRT plan using zero margin on target structures, prostate (CTV prostate ) and seminal vesicles (CTV SV ), were created, then evaluated by simulating 1000 30-fraction virtual treatment courses. Each fraction was prostate centroid aligned. Patients whose D 98 failed to achieve 95% coverage probability objective D 98,95 ≥ 78 Gy (CTV prostate ) or D 98,95 ≥ 66 Gy (CTV SV ) were replanned using planning techniques: (1) FM (PTV prostate = CTV prostate + 5 mm, PTV SV = CTV SV + 8 mm), (2) CP OM which optimized uniform PTV margins for CTV prostate and CTV SV to meet the coverage probability objective, and (3) CP COP which directly optimized coverage probability objectives for all structures of interest. These plans were intercompared by computing probabilistic metrics, including 5% and 95% percentile DVHs (pDVH) and TCP/NTCP distributions. Results: All patients were replanned using FM and two CP techniques. The selected margins used in FM failed to ensure target coverage for 8/19 patients. Twelve CP OM plans and seven CP COP plans were favored over the other plans by achieving desirable D 98,95 while sparing more normal tissues. Conclusions: Coverage-based treatment planning techniques can produce better plans than FM, while relative advantages of CP OM and CP COP are patient-specific. C 2014 American Association of Physicists in Medicine. [http://dx
Each patient was set up under daily stereoscopic x-ray and kv CBCT guidance. A single radiation oncologist retrospectively re-contoured the tumor volume on each sequential kv CBCT image and volumetric variances were recorded both in terms of cubic centimeters (CC) and Hounsfield units (HU). A univariate and Kruskall-Wallis analysis were employed using statistical software. Results: One hundred and twenty-nine NSCLC patients treated with definitive intent SBRT were identified. 72 (55.8%) patients were female and 57 (44.2%) were male. 26 (20.2%) continued to smoke during their treatment, 86 (66.7%) were former smokers, and 17 (13.2%) had never smoked before. 37 (28.7%) received steroids prior to each treatment while 92 (71.3%) did not. 90 (69.8%) were located peripherally while 39 (30.2%) were located centrally (by the RTOG 0813 definition). 59 (45.7%) patients received an SBRT dose of 54 Gy / 3 fractions while 69 (53.5%) received 50-60 Gy / 5 fractions. In the 3 fraction group, there was a median increase in tumor size of +17.64% CC (-52.78% to +225.75%), and +2.46% HU (-271.05% to +215.43%), between the first and third fractions; in the 5 fraction group, a median increase in tumor size of +10.40% CC (-50.00% to +112.62%) and +7.87% HU (-122.09% to +417.49%) was observed between the first and fifth fractions. Nine (7.0%) patients experienced local recurrence, 14 (10.9%) patients experienced locoregional recurrence, and 13 (10.1%) experienced distant recurrence. On Kruskall-Wallis Test, locoregional recurrence correlated with tumor volume change between first and third fractions in 3 fraction patients (HU) (PZ0.0440) and between the first and fifth fractions (CC) in 5 fraction patients (PZ0.0232). Conclusion: In this study, we observed interfraction tumor size changes in patients treated with SBRT. Of the patient, tumor, and dosimetric factors analyzed; locoregional recurrence significantly correlated with interfraction tumor size change between the first and last fraction. Further lung NSCLC SBRT interfraction volumetric studies are needed to further characterize the degree of tumor volume growth and thresholds that may predict recurrence.
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