AAPM-ESTRO Guidelines for Image Guided Robotic Brachytherapy: Report from Task Group 192

Volume 84  Number 3S  Supplement 2012

Oral Scientific Sessions S129

Materials/Methods: We retrospectively analyzed the results of 304 irradiations of the RPC thorax phantom at 221 different institutions as part of credentialing for RTOG clinical trials; the irradiations were all done using 6-MV beams. Heterogeneous treatment plans were developed using Monte Carlo algorithms (MC), convolution/superposition (CS) algorithms and the anisotropic analytic algorithm (AAA), as well as pencil beam (PB) algorithms. For each plan and delivery, the absolute dose measured in the center of a lung target, as well as planar dose measurements throughout the target, were compared to the calculated dose. The difference between measured and calculated dose was examined as a function of planning algorithm, as well as use of IMRT versus 3D conformal therapy, use of a motion platform versus static irradiation, and date of irradiation. Results: Only minor differences were observed in the planar agreement between different algorithms. Absolute dose agreement showed sizeable and significant variation between algorithms. PB algorithms overestimated the dose delivered to the center of the target by 4.9% on average. Surprisingly, CS algorithms and AAA also showed a systematic overestimation of the dose to the center of the target, by 3.7% on average. In contrast, the MC algorithm dose calculations agreed with measurement within 0.6% on average. There was no significant difference observed between IMRT and 3D CRT calculation accuracy, between moving or static irradiations, or as a function of time (i.e., agreement has not improved in recent years). Conclusions: Unexpectedly, advanced treatment planning systems (those using CS and AAA algorithms) overestimated the dose that was delivered to the lung target. This large dosimetric difference requires attention in terms of heterogeneity calculations and potentially in terms of clinical practice.

Results: The developed inference paradigm was first tested on the pilot site of prostate SBRT, using 97 geometric features as input, and generated output in terms of DVH curves for PTV and OAR (rectum, bladder, femoral heads, small bowel) as well as conformality measured by V(50% RX)/V(PTV). The lean regression and the kernel learning method produced superior inference performance. The identified prognostic features were observed to be mainly indicative of patient size. This is reasonable since anatomical configuration within the region of interest only varies slightly among prostate SBRT patients. The lean and kernel methods explain 21% and 82% of the plan quality variation respectively. Despite difference in details, both inference methods successfully identified the 3 plans (out of the 29 cohort) that exhibited higher-than-usual PTV hot spots. Conclusions: An inference paradigm has been developed to generate an “expectation” of individual plan quality based on treated population and geometric similarity. Such expectation can be obtained before the actual planning, providing reference goals for the planner to strive for standardization and a baseline for detecting abnormality. Sparse regression automatically discovers prognostic features for plan quality, and may be used to set sub-population specific plan quality goals. The developed cross validation method provides a systematic way for consistency evaluation. The current paradigm extends naturally to incorporate clinical outcome data. Author Disclosure: D. Ruan: None. W. Shao: None. J. DeMarco: None. S. Tenn: None. N. Agazaryan: None. C. King: None. D. Low: None. P. Kupelian: None. M. Steinberg: None.

321 Oral Scientific Abstract 319; Table measured to calculated dose Algorithm Mean Standard Deviation

Mean ratio and standard deviation of

Monte Carlo AAA Pinnacle 0.994 0.030

0.959 0.027

0.968 0.024

Xio

Tomotherapy

PB

0.955 0.025

0.971 0.024

0.951 0.027

Author Disclosure: S. Kry: None. P. Alvarez: None. A. Molineu: None. C. Amador: None. J. Galvin: None. D. Followill: None.

AAPM-ESTRO Guidelines for Image Guided Robotic Brachytherapy: Report from Task Group 192 T. Podder,1 L. Beaulieu,2 A. Dicker,3 M. Meltsner,4 M. Moerland,5 R. Nath,6 M. Rivard,7 D. Song,8 B. Thomadsen,9 and Y. Yu3; 1East Carolina University, Greenville, NC, 2Centre Hospitalier Universite´ de Quebec, Quebec, QC, Canada, 3Thomas Jefferson University, Philadelphia, PA, 4Philips Radiation Oncology Systems, Fitchburg, WI, 5 University Medical Center Utrecht, Utrecht, Netherlands, 6Yale University School of Medicine, New Haven, CT, 7Tufts University School of Medicine, Boston, MA, 8Johns Hopkins University School of Medicine, Baltimore, MD, 9University of Wisconsin, Madison, WI

320 Plan Quality Inference and Cross Validation for Standardization and Consistency Evaluation D. Ruan, W. Shao, J. DeMarco, S. Tenn, N. Agazaryan, C. King, D. Low, P. Kupelian, and M. Steinberg; University of California, Los Angeles, Los Angeles, CA Purpose/Objective(s): To develop plan quality inference methods to generate expected plan quality based on treated populations; to establish a cross-validation framework to generate consistency metrics. Materials/Methods: An inference paradigm has been developed using a large input feature set of potential prognostic powers, specifically (1) the volumes of each PTV/OAR structure, (2) the minimal 3D distance and/or overlapping volume between pairs of structures, and (3) the centroid location distance between pairs of structures. The inference outputs expected plan qualities in terms of DVH values and conformality indices. Four regression approaches have been developed within the inference paradigm: (1) a full regression utilizing all input; (2) a sparse regression penalizing the number of contributing prognostic features; (3) a lean regression limited to the features discovered by the sparse regression; and (4) a kernel method that adaptively “learns” the plan quality from treated subjects with similar geometries. A leave-one-out cross validation method was developed comparing the inferred plan quality of a subject based on the other subjects in the cohort and the actually achieved plan quality. Practice consistency is characterized by integrating such discrepancies in the cohort.

Purpose/Objective(s): To present the preliminary recommendations of the AAPM-ESTRO task group 192 (TG-192) which was charged in 2009 to review the state-of-the-art of robotic systems developed for interstitial brachytherapy and to recommend commissioning and quality assurance procedures for safe and consistent clinical use of these systems. Materials/Methods: To date, 13 robotic brachytherapy systems have been developed incorporating a variety of imaging, control, and delivery techniques. These systems aim for higher accuracy and precision in source placement, reduction of surgical trauma, and minimization of learning burden for the optimum delivery of sources. Here, we focus on the application of this technology to the implantation of radioactive sources in patients with early stage prostate cancer. Considering the recent developments in this area, the American Association of Physicists in Medicine (AAPM) commissioned TG-192 in 2009 to review the state-of-the-art in the field of robot-assisted interstitial brachytherapy. Later, the European Society for Radiotherapy and Oncology (ESTRO) joined this group. The task group addresses the unique challenges posed by the characteristics of robotic behavior, as well as interactions between robots and human clinicians in the complex intraoperative setting. The task group has developed a protocol for evaluating the performance of any robotic brachytherapy system for seed implantation. Using this protocol, the parameters to be evaluated are positioning accuracy and repeatability of needle tip, accuracy of source delivery, accuracy of spatial and temporal calibration between the robot and the

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International Journal of Radiation Oncology  Biology  Physics

imager, and qualitative assessment of the tissue damage if the needle is rotated during insertion. Results: Conventional manual source placement techniques with a stationary physical template have an estimated accuracy of 2-3 mm. This varies due to multiple factors, such as physician’s skill level, tissue deformation, needle bending, and edema, etc. Based on the performance of the existing robotic brachytherapy systems, the TG-192 has recommended to the AAPM and ESTRO boards that robotic systems should have a spatial accuracy of at least 1 mm in source placement in phantoms. Report of the TG-192 and these recommendations are waiting for the approval of AAPM and ESTRO. Conclusions: Preliminary recommendations are that specified tests should be conducted during clinical commissioning of a robotic brachytherapy system to ensure that sub-millimeter level of accuracy is maintained. These tests should mimic the real operating procedure as closely as possible and should follow the test protocol incorporated in the report of the TG-192. Author Disclosure: T. Podder: None. L. Beaulieu: None. A. Dicker: None. M. Meltsner: None. M. Moerland: None. R. Nath: None. M. Rivard: None. D. Song: None. B. Thomadsen: None. Y. Yu: None.

wall were calculated. The change in TCP/NTCP caused by different type of errors was quantified. Results: The DTCP and DNTCP of each specific type of error for each specific ROI as averaged by all patient cases over all simulated errors are shown in the Table. Conclusions: Our modeling study quantifies the impact of several different types of TPS modeling errors and machine delivery errors on TCP and NTCP. The change in TCP and NTCP varies through different error types and different ROIs, which could be a combined effect of the change in dose delivered to the organ and the region of TCP/NTCP curve from the original plan. DTCP and DNTCP serve as an objective tool in quantifying the clinical impact of TPS and delivery errors in IMRT. Author Disclosure: H. Zhen: None. B.E. Nelms: None. W.A. Tome´: None.

322 The Impact of TPS and Machine Delivery Errors on Clinical Outcomes as Estimated by Biological Models H. Zhen,1 B.E. Nelms,2 and W.A. Tome´1; 1University of Wisconsin, Madison, WI, 2Canis Lupus LLC, Merrimac, WI Purpose/Objective(s): The purpose of this work is to investigate the clinical impact of several different types of: 1) Treatment Planning System (TPS) modeling errors and 2) machine delivery errors, by analysis using mathematical models of Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP). Materials/Methods: Twenty Head and Neck (HN) cancer patients and 20 prostate cancer patients previously approved and treated with external beam radiation therapy using step and shoot IMRT were adopted for this study. To simulate TPS machine modeling errors, six different types of machine model errors (MLC transmission high and low, MLC penumbra shallow and very shallow, and Tongue and Groove width wide and narrow) were induced in a gold-standard beam model in our TPS. For each simulation of a TPS error, an IMRT plan was generated using each of the error induced beam model (called this dose “planned”) and then recalculated using the gold-standard beam model (call this dose “virtually delivered”). To simulate machine delivery errors, an IMRT plan was generated using the gold standard beam model, and then the “virtually delivered” patient dose was generated by recalculating the plan with specific modifications (machine output 3% high and 3% low, systematic MLC position error of +/-1mm for each bank). From the planned and virtually delivered patient dose distribution, the TCP for targets and NTCP for several critical organs including spinal cord, both parotids, larynx, and rectal

Oral Scientific Abstract 322; Table

CTV (HN) GTV (HN) CTV (Prostate) Contralateral Parotid Ipsilateral Parotid Spinal Cord Larynx Rectal Wall

323 The Weakest Link e Medical Physicists Need Dedicated Software and Further Training to Improve the Quality of Rotational IMRT O. De Hertogh,1 T.J. Bichay,2 R. Viard,3 B. Germain,3 and Y. Boukour1; 1 Centre Hospitalier Peltzer - La Tourelle, Verviers, Belgium, 2Saint Mary’s Health Care, Grand Rapids, MI, 3AquiLab SAS, Lille, France Purpose/Objective(s): An earlier study (De Hertogh et al., ESTRO 2011) compared planning of 10 locally-advanced HNSCC (T3-4 N2-3) by 10 experienced TomoTherapy users. The medical physicists involved (users group 1) achieved heterogeneous results regarding dose homogeneity within the PTV and sparing of the surrounding non-tumor tissue (NTT). Discrepancies by up-to-20% in integral dose (ID) were related to a lack of conformity of the reference isodose to the PTV, i.e., a low overlap ratio (OR) and dice similarity coefficient (DSC), and an insufficient steepness of the dose fall-off, i.e., a low dose gradient (DG). Comparable results were achieved by other rotational techniques, (Boukour et al., ESTRO 2011). Materials/Methods: To reduce the variability in achieved results among rotational IMRT users, a dedicated analysis software was conceived by AquiLab SAS (Lille, France). The software allowed the use of PTV dose homogeneity indicators, OR, DSC and DG during plan optimization. The same 10 locally-advanced HNSCC cases were planned by 10 new (naı¨ve) European and American TomoTherapy centers (users group 2). Plans were deemed acceptable if achieving a 65% increase in PTV dose homogeneity compared to ICRU-62 minimal requirements; an OR above 0.75 and a DSC above 0.86; and a DG steeper than 2.5%/mm for bilateral volumes and 3.2%/mm for unilateral volumes. Medical physicists received an onsite two-day training to learn using the software and its parameters, before planning the patients on their own. Results: Between users groups 1 and 2, the increase in dose homogeneity within the PTV was significant (p Z 0.002), with mean STD values of 1.012 Gy (0.814-1.185) and 0.824 Gy (0.676-0.973) respectively. The increase in dose conformity to the PTV was significant (p Z 0.002), with mean OR values of 0.756 (0.715-0.801) and 0.815 (0.738-0.843) respectively. Use of the DSC showed the same significant difference. Although

Average change in TCP/NTCP of each specific ROI and specific type of error

Machine Output High

Machine Output Low

MLC Gap Wide

MLC Gap Narrow

MLC Penumbra Shallow

MLC Penumbra Very Shallow

MLC Transmission High

MLC Transmission Low

2.17% 4.18% 4.44% 1.19%

-2.63% -5.23% -5.70% -1.16%

1.22% 1.99% 1.10% 2.08%

-1.44% -2.55% -1.22% -1.80%

0.05% 0.66% -0.16% -0.85%

0.21% 1.11% -0.09% -1.34%

-1.30% -2.17% -0.86% -2.01%

1.30% 2.17% 0.93% 2.36%

1.73%

-1.92%

1.48%

-1.49%

0.14%

0.17%

-1.39%

1.50%

0.10% 0.76% 5.02%

-0.08% -0.61% -4.09%

0.08% 1.42% 2.24%

-0.05% -0.88% -2.12

-0.07% -1.17% 2.40%

-0.11% 1.27% 3.33%

-0.07% -0.88% -0.93%

0.11% 1.25% 1.14%