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Optimization of Robotic Radiosurgery Dosimetric Planning Using a Dose-Limiting Auto-Shell Method for Brain Metastases
Poster Viewing E745
Volume 99 Number 2S Supplement 2017 Author Disclosure: V. Yau: None. P.E. Lindsay: None. A.J. Hope: Honoraria; Elekta. Travel Expenses; Elekta. L.W. Le: None. D. Glick: None. A. Lau: None. J. Cho: None. A. Bezjak: Leader of the national professional organization in the previous year – now still on executive, but as “past president”; Canadian Association of Radiation Oncology (CARO). A. Sun: None. M.E. Giuliani: Honoraria; Elekta Inc. Travel Expenses; Elekta Inc. ; Canadian Association of Radiation Oncology.
3769 A Software Application to Evaluate the Dosimetric Sensitivity of Individual Stereotactic Radiosurgery Plans to Positional Variations A. Yock1 and G.Y. Kim2; 1Vanderbilt University Medical Center Nashville, TN, 2University of California, San Diego, La Jolla, CA Purpose/Objective(s): In stereotactic radiosurgery, the dosimetric effects of positional variations depend on patient- and plan-specific factors. This work demonstrates the use of in-house software to provide a statistical analysis of changes due to positional variations for any SRS plan. The software gives 3D dose distributions and DVHs based on data exported from the treatment planning system. We demonstrate its use by comparing the probabilistic dose distributions for an example multimet SRS patient under several treatment scenarios featuring different numbers of isocenters and different positional variation action levels. Materials/Methods: We developed a software application that considers DICOM files from a commercial treatment planning system. The application was used to sample 1,000 geometric transformations from a continuous, highly-flexible, user-defined, patient motion model. Dosimetric effects of the transformations were simulated by sampling the affected target volume from a static dose cloud. The application was validated using known doses calculated by the treatment planning system under known transformations. Using the validated application, we assessed the dosimetric effects of multiple treatment scenarios on four previously treated patients that varied in number of targets and target proximity to organs at risk. While the application provides 3D dose distributions and DVHs, we evaluated the change in the volume of the target receiving at least the prescription dose (DV100%). Presented are example results for one of the patients. We compared the effect of treating this patient’s two mets individually vs. treating them simultaneously, as well as using 1 mm & 1 vs. 2 mm & 2 positional variation action levels. Results: When the two mets of the example patient were treated with a single isocenter and 1 mm & 1 action levels, the minimum, median, and interquartile range of DV100% was -28.7%/-23.2%, -6.6%/-6.6%, and 7.4%/6.3%, respectively, for met 1/met 2. When each met was treated with an individual isocenter, the values were -8.2%/-13.3%, -3.0%/-3.8%, and 3.2%/4.0%. When treated with a single isocenter and 2 mm & 2 action levels, the values were -39.7%/-33.8%, -9.6%/-8.7%, and 9.7%/8.5%. Conclusion: The sensitivity of the delivered dose distribution to positional variations was measured using DV100% for an example patient under several treatment scenarios. As expected, treating the two mets with a single isocenter and using more tolerant action levels resulted in larger dosimetric changes. While the observed effects are aligned with generalized expectations, our approach statistically considers patient- and planspecific factors and provides clinicians with a probabilistic delivered dose distribution. If used during treatment planning and delivery, this approach could directly result in SRS plans more robust to positional variations. Author Disclosure: A. Yock: None. G. Kim: None.
3770 Optimization of Robotic Radiosurgery Dosimetric Planning Using a Dose-Limiting Auto-Shell Method for Brain Metastases K. Yoon,1 B. Cho,2 J. Kwak,3 D. Lee,1 D.H. Kwon,4 S.D. Ahn,5 S.W. Lee,2 C.J. Kim,4 S.W. Roh,4 and Y.H. Cho4; 1Radiosurgery center, Asan Medical Center, Seoul, Korea, Republic of (South), 2Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, Republic of (South), 3Department of Radiation Oncology, Asan Medical Center, Seoul, Korea, Republic of (South), 4Neurosurgery,
Asan Medical Center, Seoul, Korea, Republic of (South), 5Radiation Oncology, Asan Medical Center, Seoul, Korea, Republic of (South) Purpose/Objective(s): We investigated the impact of optimization in dose-limiting auto-shell function on the dosimetric quality of Cyberknife (CK) plans in treating brain metastases (BMs). Materials/Methods: We selected 19 BMs previously treated using CK between 2014 and 2015. The original CK plans (CKoriginal) had been produced using one to 3 dose-limiting auto-shells, one at the prescription dose (PD) level for dose conformity and others at low-dose levels (10e30% of PD) for dose spillage. In each case, a modified CK plan (CKmodified) was generated using 5 dose-limiting auto-shells, one at PD level, another at intermediate dose level (50% of PD) for steeper dose fall-off, and the others at low-dose levels, with an optimized shell-dilation size based on our experience. A Gamma Knife (GK) plan was also produced using the original contour set. Thus, a triplet data set of dosimetric parameters was generated and compared. Results: There were no differences among CKoriginal, CKmodified, and GK plans in the conformity index (mean 1.22, 1.18, and 1.24, respectively; PZ0.079) and tumor coverage (mean 99.5%, 99.5%, and 99.4%, respectively; PZ0.177), whereas CKmodified plans produced significantly smaller normal tissue volumes receiving 50% of PD than those produced by CKoriginal plans (P<0.001), with no statistical differences in those volumes compared with GK plans (PZ0.345) Conclusion: These results indicate that significantly steeper dose fall-off can be further achieved in the CK system by optimizing the auto-shell function, while maintaining high conformity of dose to tumor. Author Disclosure: K. Yoon: None. B. Cho: None. J. Kwak: None. D. Lee: None. D. Kwon: None. S. Ahn: None. S. Lee: None. C. Kim: None. S. Roh: None. Y. Cho: None.
3771 Withdrawn
3772 Changes in Multimodality MRI Characteristics Following SBRT in Prostate Cancer K. Zakian,1 H.A. Vargas,1 A. Iyer,1 N. Tyagi,1 A. Apte,1 M.A. Kollmeier,1 B.R. Mychalczak,1 K.L. Borofsky,2 O. Cahlon,1 M.A. Hunt,1 E. Sala,1 and M.J. Zelefsky1; 1Memorial Sloan Kettering Cancer Center, New York, NY, 2Memorial Sloan Kettering Cancer Center, New York, NY, United States Purpose/Objective(s): Utility of PSA as an early response marker in prostate cancer (PCa) after RT is limited due to variability in time to nadir. We currently are accruing patients undergoing 5 RT regimens to evaluate multi-modality MRI for characterization of PCa changes following RT and potential correlation with response. Materials/Methods: As part of an IRB-approved study, patients undergoing RT for PCa underwent MRI with diffusion and perfusion measurements prior to any treatment, following ADT/prior to RT (if applicable), and 3, 6, 12, 18, and 24 months after completion of RT. Inclusion criteria for the study included biopsy proven adenocarcinoma, MR-visible tumor 0.5 cm, and no metastatic disease. This study reports the results in 11 patients who underwent hypofractionated SBRT (5 x 800cGy) with followup MRI to 6 months. MR Imaging. All imaging was performed on a 3.0 Tesla Philips Ingenia MR. In addition to standard anatomical T1 and T2-weighted imaging, subjects underwent dynamic contrast-enhanced (DCE) perfusion imaging (3D SPGR, voxel size Z 1.0 x 1.0 x 4mm3, TR/ TE Z 4ms/2ms, frame length Z 5-6 s, flip angle 8 ) and multi-b-value diffusion-weighted imaging (single-shot EPI, 16 b-values between 0 and 1000 s/mm2], transverse plane, voxel size 1.1 x 1.1 x 4.5 mm3). Tumor regions-of-interest were identified by GU expert radiologists. If tumor was not detectable in post-treatment images, the ROI was placed in the pretreatment tumor location. ROI analysis software was developed in Matlab and incorporated in CERR [1]. DCE data were fit to the extended Tofts model [2] on a voxel-by-voxel basis, and Ktrans, ve, and Kep were calculated. Apparent diffusion coefficient (ADC) was calculated by fitting data
Volume 99 Number 2S Supplement 2017 Author Disclosure: V. Yau: None. P.E. Lindsay: None. A.J. Hope: Honoraria; Elekta. Travel Expenses; Elekta. L.W. Le: None. D. Glick: None. A. Lau: None. J. Cho: None. A. Bezjak: Leader of the national professional organization in the previous year – now still on executive, but as “past president”; Canadian Association of Radiation Oncology (CARO). A. Sun: None. M.E. Giuliani: Honoraria; Elekta Inc. Travel Expenses; Elekta Inc. ; Canadian Association of Radiation Oncology.
3769 A Software Application to Evaluate the Dosimetric Sensitivity of Individual Stereotactic Radiosurgery Plans to Positional Variations A. Yock1 and G.Y. Kim2; 1Vanderbilt University Medical Center Nashville, TN, 2University of California, San Diego, La Jolla, CA Purpose/Objective(s): In stereotactic radiosurgery, the dosimetric effects of positional variations depend on patient- and plan-specific factors. This work demonstrates the use of in-house software to provide a statistical analysis of changes due to positional variations for any SRS plan. The software gives 3D dose distributions and DVHs based on data exported from the treatment planning system. We demonstrate its use by comparing the probabilistic dose distributions for an example multimet SRS patient under several treatment scenarios featuring different numbers of isocenters and different positional variation action levels. Materials/Methods: We developed a software application that considers DICOM files from a commercial treatment planning system. The application was used to sample 1,000 geometric transformations from a continuous, highly-flexible, user-defined, patient motion model. Dosimetric effects of the transformations were simulated by sampling the affected target volume from a static dose cloud. The application was validated using known doses calculated by the treatment planning system under known transformations. Using the validated application, we assessed the dosimetric effects of multiple treatment scenarios on four previously treated patients that varied in number of targets and target proximity to organs at risk. While the application provides 3D dose distributions and DVHs, we evaluated the change in the volume of the target receiving at least the prescription dose (DV100%). Presented are example results for one of the patients. We compared the effect of treating this patient’s two mets individually vs. treating them simultaneously, as well as using 1 mm & 1 vs. 2 mm & 2 positional variation action levels. Results: When the two mets of the example patient were treated with a single isocenter and 1 mm & 1 action levels, the minimum, median, and interquartile range of DV100% was -28.7%/-23.2%, -6.6%/-6.6%, and 7.4%/6.3%, respectively, for met 1/met 2. When each met was treated with an individual isocenter, the values were -8.2%/-13.3%, -3.0%/-3.8%, and 3.2%/4.0%. When treated with a single isocenter and 2 mm & 2 action levels, the values were -39.7%/-33.8%, -9.6%/-8.7%, and 9.7%/8.5%. Conclusion: The sensitivity of the delivered dose distribution to positional variations was measured using DV100% for an example patient under several treatment scenarios. As expected, treating the two mets with a single isocenter and using more tolerant action levels resulted in larger dosimetric changes. While the observed effects are aligned with generalized expectations, our approach statistically considers patient- and planspecific factors and provides clinicians with a probabilistic delivered dose distribution. If used during treatment planning and delivery, this approach could directly result in SRS plans more robust to positional variations. Author Disclosure: A. Yock: None. G. Kim: None.
3770 Optimization of Robotic Radiosurgery Dosimetric Planning Using a Dose-Limiting Auto-Shell Method for Brain Metastases K. Yoon,1 B. Cho,2 J. Kwak,3 D. Lee,1 D.H. Kwon,4 S.D. Ahn,5 S.W. Lee,2 C.J. Kim,4 S.W. Roh,4 and Y.H. Cho4; 1Radiosurgery center, Asan Medical Center, Seoul, Korea, Republic of (South), 2Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, Republic of (South), 3Department of Radiation Oncology, Asan Medical Center, Seoul, Korea, Republic of (South), 4Neurosurgery,
Asan Medical Center, Seoul, Korea, Republic of (South), 5Radiation Oncology, Asan Medical Center, Seoul, Korea, Republic of (South) Purpose/Objective(s): We investigated the impact of optimization in dose-limiting auto-shell function on the dosimetric quality of Cyberknife (CK) plans in treating brain metastases (BMs). Materials/Methods: We selected 19 BMs previously treated using CK between 2014 and 2015. The original CK plans (CKoriginal) had been produced using one to 3 dose-limiting auto-shells, one at the prescription dose (PD) level for dose conformity and others at low-dose levels (10e30% of PD) for dose spillage. In each case, a modified CK plan (CKmodified) was generated using 5 dose-limiting auto-shells, one at PD level, another at intermediate dose level (50% of PD) for steeper dose fall-off, and the others at low-dose levels, with an optimized shell-dilation size based on our experience. A Gamma Knife (GK) plan was also produced using the original contour set. Thus, a triplet data set of dosimetric parameters was generated and compared. Results: There were no differences among CKoriginal, CKmodified, and GK plans in the conformity index (mean 1.22, 1.18, and 1.24, respectively; PZ0.079) and tumor coverage (mean 99.5%, 99.5%, and 99.4%, respectively; PZ0.177), whereas CKmodified plans produced significantly smaller normal tissue volumes receiving 50% of PD than those produced by CKoriginal plans (P<0.001), with no statistical differences in those volumes compared with GK plans (PZ0.345) Conclusion: These results indicate that significantly steeper dose fall-off can be further achieved in the CK system by optimizing the auto-shell function, while maintaining high conformity of dose to tumor. Author Disclosure: K. Yoon: None. B. Cho: None. J. Kwak: None. D. Lee: None. D. Kwon: None. S. Ahn: None. S. Lee: None. C. Kim: None. S. Roh: None. Y. Cho: None.
3771 Withdrawn
3772 Changes in Multimodality MRI Characteristics Following SBRT in Prostate Cancer K. Zakian,1 H.A. Vargas,1 A. Iyer,1 N. Tyagi,1 A. Apte,1 M.A. Kollmeier,1 B.R. Mychalczak,1 K.L. Borofsky,2 O. Cahlon,1 M.A. Hunt,1 E. Sala,1 and M.J. Zelefsky1; 1Memorial Sloan Kettering Cancer Center, New York, NY, 2Memorial Sloan Kettering Cancer Center, New York, NY, United States Purpose/Objective(s): Utility of PSA as an early response marker in prostate cancer (PCa) after RT is limited due to variability in time to nadir. We currently are accruing patients undergoing 5 RT regimens to evaluate multi-modality MRI for characterization of PCa changes following RT and potential correlation with response. Materials/Methods: As part of an IRB-approved study, patients undergoing RT for PCa underwent MRI with diffusion and perfusion measurements prior to any treatment, following ADT/prior to RT (if applicable), and 3, 6, 12, 18, and 24 months after completion of RT. Inclusion criteria for the study included biopsy proven adenocarcinoma, MR-visible tumor 0.5 cm, and no metastatic disease. This study reports the results in 11 patients who underwent hypofractionated SBRT (5 x 800cGy) with followup MRI to 6 months. MR Imaging. All imaging was performed on a 3.0 Tesla Philips Ingenia MR. In addition to standard anatomical T1 and T2-weighted imaging, subjects underwent dynamic contrast-enhanced (DCE) perfusion imaging (3D SPGR, voxel size Z 1.0 x 1.0 x 4mm3, TR/ TE Z 4ms/2ms, frame length Z 5-6 s, flip angle 8 ) and multi-b-value diffusion-weighted imaging (single-shot EPI, 16 b-values between 0 and 1000 s/mm2], transverse plane, voxel size 1.1 x 1.1 x 4.5 mm3). Tumor regions-of-interest were identified by GU expert radiologists. If tumor was not detectable in post-treatment images, the ROI was placed in the pretreatment tumor location. ROI analysis software was developed in Matlab and incorporated in CERR [1]. DCE data were fit to the extended Tofts model [2] on a voxel-by-voxel basis, and Ktrans, ve, and Kep were calculated. Apparent diffusion coefficient (ADC) was calculated by fitting data