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.
Purpose:
To use a population‐based statistical motion model to create a patient‐specific planning target volume (PTV) for patients undergoing radiotherapy for definitive treatment of low‐risk prostate cancer and to compare the dosimetric impact of this PTV against current clinically used PTVs.
Methods:
From mapping 10–13 fractional CT images to the planning image, systematic and random deformations were calculated for 19 prostate patients. These deformations were mapped to a reference image using inter‐patient deformable image registration (DIR). In the reference image coordinate system, principal component analysis (PCA) was used to create 3D statistical motion models of the systematic and random displacements. Using these models to sample synthetic displacements and mapping them back to a patient's planning coordinate system, organ occupancy maps were created for the prostate. The organ occupancy map was thresholded at the 95% coverage level to create a PCA‐based PTV. For bony aligned and prostate centroid aligned patient setups, a virtual clinical trial was conducted to determine the dosimetric differences between a PCA‐based PTV and a conventional PTV (using a van Herk margin for bony alignment and 5 mm fixed [3 mm posterior] for centroid alignment). Dose volume histogram (DVH) metrics were used in the analysis.
Results:
All PTVs provided prescription dose coverage to the prostate. For bony aligned setup, the PCA‐based PTV significantly (p<0.05) reduced D₃₀, D₂₀, and D5 to bladder and D5₀ to rectum, while increasing rectal D₂₀ and D5. For the centroid aligned setup, the PCA PTV significantly reduced all bladder DVH metrics and trended to lower rectal DVH metrics.
Conclusion:
Statistical motion modeling provides a systematic method for creating PTVs. Plans created using these PTVs provide adequate prescription dose coverage of the prostate while lowering the bladder dose. PCA‐based PTVs tended to increase rectal dose for bony‐aligned patients and lower it for centroid aligned patients.
SBRT compared to 20.2 months in the combined group (HR¼1.3; p<0.001; CI: 1.22-1.44). Conclusion: SBRT should be the sole modality treatment for patients with inoperable stage I NSCLC. However, patients with stage II disease appear to benefit from adjuvant chemotherapy. Randomized trials are needed in this area to answer this question conclusively.
Purpose:
Assess the correct implementation of risk‐based methodology of TG 100 to optimize quality management and patient safety procedures for Stereotactic Body Radiation Therapy.
Methods:
A detailed process map of SBRT treatment procedure was generated by a team of three physicists with varying clinical experience at our institution to assess the potential high‐risk failure modes. The probabilities of occurrence (O), severity (S) and detectability (D) for potential failure mode in each step of the process map were assigned by these individuals independently on the scale from1 to 10. The risk priority numbers (RPN) were computed and analyzed. The highest 30 potential modes from each physicist's analysis were then compared.
Results:
The RPN values assessed by the three physicists ranged from 30 to 300. The magnitudes of the RPN values from each physicist were different, and there was no concordance in the highest RPN values recorded by three physicists independently. The 10 highest RPN values belonged to sub steps of CT simulation, contouring and delivery in the SBRT process map. For these 10 highest RPN values, at least two physicists, irrespective of their length of experience had concordance but no general conclusions emerged.
Conclusion:
This study clearly shows that the risk‐based assessment of a clinical process map requires great deal of preparation, group discussions, and participation by all stakeholders. One group albeit physicists cannot effectively implement risk‐based methodology proposed by TG100. It should be a team effort in which the physicists can certainly play the leading role. This also corroborates TG100 recommendation that risk‐based assessment of clinical processes is a multidisciplinary team effort.
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