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An Automatic Learning Technique for Parameter Optimization in Inverse Planning
Proceedings of the 49th Annual ASTRO Meeting
2874
Evaluation of the Accuracy of Cone-Beam Based Patient Positioning for Treatment of Spinal Lesions and Efficacy of Adaptive Strategies to Maximize the Cord Sparing
A. Harrison, G. Bednarz, J. M. Galvin Dept. of Radiation Oncology, Thomas Jefferson Univ., Philadelphia, PA Purpose/Objective(s): To investigate the accuracy of patient positioning with cone beam guidance for the treatment of spinal lesions; to evaluate adaptive conformal avoidance strategies to maximize the cord sparing. Materials/Methods: Elekta’s cone-beam (CB) device provides 3D CT imaging information. Compared to stereoscopic x-ray imaging, volume imaging makes it possible to not only adjust patient position before commencing the treatment, but also to evaluate the accuracy of the rigid body-based correction in cases where there is organ deformation. This can be important in high dose per fraction treatment of metastatic lesions abutting the spinal cord. The accuracy of the cord alignment for the registration Region-ofInterest (ROI) encompassing several vertebrae was investigated for thirteen patients treated using either Elekta’s stereotactic body frame (T- and L-spine location of ROI) or with a head-and-neck thermoplastic mask (C-spine ROI). The effect of small misalignment in the cord position due to changes in its curvature on this organ’s dose-volume-histogram (DVH) was investigated for selected cases. Two simple adaptive on-line correction strategies to maximize cord sparing and bypass the re-planning stage were evaluated. One strategy relied on the aperture-based-planning and on-line aperture shape modification based on the cone-beam imaging to protect the cord. The other strategy used beamlet-based IMRT planning to create additional IMRT plan(s), which took into account a possible small movement of the cord-lesion border. Results: In the majority of analyzed cone beam scans the registration resulted in a very good cord alignment when the ROI extended over three to four vertebrae, which was the assumed extent of the spinal lesion plus margins. For approximately 5% of the scans, the maximum difference in the range from 1 to 3 mm was observed between the planning CT and CB cords using the spinal canal edge as a surrogate for the cords position. For approximately 2 mm cord shift, changing the shapes of the treatment apertures to shield the cord lowered the maximum cord dose by an average 5%. From the cord dose-volume-histograms (DVHs), the 5% of the cord volume above a threshold dose was decreased to approximately 1.5% of the cord volume. Selecting an IMRT plan to better fit the CB cord position had similar effect. The maximum cord dose was decreased by an average 7% and the 5% high-dose cord volume was decreased to approximately 1.5% of the volume. Conclusions: Elekta’s cone beam device could be used effectively to achieve high positioning accuracy for the patients treated for spinal metastases. For a low percentage of cases, the rigid body registration and position correction did not account for small changes in spine curvature. Simple adaptive correction schemes can be applied to further protect the cord without the need for the treatment plan re-optimization. Author Disclosure: A. Harrison, None; G. Bednarz, None; J.M. Galvin, None.
2875
An Automatic Learning Technique for Parameter Optimization in Inverse Planning
1
F. Stieler , H. Yan2, C. G. Willett2, F. Yin2 1
Universitaetsklinikum Mannheim, Mannheim, Germany, 2Duke University, Durham, NC
Purpose/Objective(s): Inverse planning often needs manual adjustment of parameters to compromise between target and critical organ doses. This process could be replaced by an automated approach such as fuzzy inference system (FIS) based on fuzzy logic. The fuzzy logic approach requires some pre-selected parameters based on prior knowledge. In order to optimize parameter selection in inverse planning of intensity-modulated radiation therapy, we are developing an automated learning technique called adaptive Neuro-Fuzzy Inference System (ANFIS). Materials/Methods: Conventionally, the model parameters of a FIS were manually pre-selected by human and then applied this pre-configured FIS on the parameter optimization of inverse planning. For making these model parameters optimal, a trial-anderror method was usually employed and an extended fine-tuning process of these model parameters is inevitable. For making this step automated and optimal, a new type of FIS, called ANFIS, is introduced and own the capability of learning model parameters from training samples. Whenever an input is presented to ANFIS, the output will be calculated and compared with the target of sample. The developed ANFIS system is interfaced with a commercial inverse planning system and examined with a real clinical case. Three inverse plans were generated using three different methods to optimize parameter selection by human planner, original FIS (manually specified model parameters), ANFIS, respectively. Results: Three strategies for training ANFIS were investigated. The ANFIS 1 was trained by 50 training and validation samples. The ANFIS 2 was trained by 100 training samples. The ANFIS 3 was trained by 100 training and validation samples. The all three ANFIS demonstrated the similar prediction errors. The results of a clinical case are presented in Fig. 1 to 3. The dose volume histograms (DVH) between plans conducted by original FIS and ANFIS were comparable as shown in Fig. 1. Compared with the plan made by human planner, the DVHs obtained from FIS plans were apparently improved. Dose sparing of rectum and bladder were observed while the similar dose coverage on PTV were maintained as shown in Fig. 2 and Fig. 3. Conclusions: The ANFIS demonstrated the capability of learning model parameters from training data. The preliminary clinical result indicated that the performance of ANFIS is comparable to those of the original FIS but no fine-tuning procedure of model parameters is necessary. As ANFIS makes the configuration of model parameters of FIS automated and optimal, it will greatly improve the efficiency of current parameter optimization procedure of inverse planning and save more time for planners on treatment planning. Author Disclosure: F. Stieler, None; H. Yan, None; C.G. Willett, None; F. Yin, None.
2876
Stick Around, Don’t Split: A Novel Approach to Reducing the Number of Split Fields in Large Field IMRT
1
C. C. Lee , A. Wu2, M. Garg1, S. Mutyala1, S. Kalnicki1, D. Mah1 1 Montefiore Medical Center, Bronx, NY, 2Department of Radiologic Sciences, Thomas Jefferson University, Philadelphia, PA Purpose/Objective(s): IMRT has been applied to head and neck, lung, pelvis and high-risk prostate cancer treatments. The design of the Varian MLC requires that fields with widths greater than 14 cm be split into two or more carriage movements. With the split
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2874
Evaluation of the Accuracy of Cone-Beam Based Patient Positioning for Treatment of Spinal Lesions and Efficacy of Adaptive Strategies to Maximize the Cord Sparing
A. Harrison, G. Bednarz, J. M. Galvin Dept. of Radiation Oncology, Thomas Jefferson Univ., Philadelphia, PA Purpose/Objective(s): To investigate the accuracy of patient positioning with cone beam guidance for the treatment of spinal lesions; to evaluate adaptive conformal avoidance strategies to maximize the cord sparing. Materials/Methods: Elekta’s cone-beam (CB) device provides 3D CT imaging information. Compared to stereoscopic x-ray imaging, volume imaging makes it possible to not only adjust patient position before commencing the treatment, but also to evaluate the accuracy of the rigid body-based correction in cases where there is organ deformation. This can be important in high dose per fraction treatment of metastatic lesions abutting the spinal cord. The accuracy of the cord alignment for the registration Region-ofInterest (ROI) encompassing several vertebrae was investigated for thirteen patients treated using either Elekta’s stereotactic body frame (T- and L-spine location of ROI) or with a head-and-neck thermoplastic mask (C-spine ROI). The effect of small misalignment in the cord position due to changes in its curvature on this organ’s dose-volume-histogram (DVH) was investigated for selected cases. Two simple adaptive on-line correction strategies to maximize cord sparing and bypass the re-planning stage were evaluated. One strategy relied on the aperture-based-planning and on-line aperture shape modification based on the cone-beam imaging to protect the cord. The other strategy used beamlet-based IMRT planning to create additional IMRT plan(s), which took into account a possible small movement of the cord-lesion border. Results: In the majority of analyzed cone beam scans the registration resulted in a very good cord alignment when the ROI extended over three to four vertebrae, which was the assumed extent of the spinal lesion plus margins. For approximately 5% of the scans, the maximum difference in the range from 1 to 3 mm was observed between the planning CT and CB cords using the spinal canal edge as a surrogate for the cords position. For approximately 2 mm cord shift, changing the shapes of the treatment apertures to shield the cord lowered the maximum cord dose by an average 5%. From the cord dose-volume-histograms (DVHs), the 5% of the cord volume above a threshold dose was decreased to approximately 1.5% of the cord volume. Selecting an IMRT plan to better fit the CB cord position had similar effect. The maximum cord dose was decreased by an average 7% and the 5% high-dose cord volume was decreased to approximately 1.5% of the volume. Conclusions: Elekta’s cone beam device could be used effectively to achieve high positioning accuracy for the patients treated for spinal metastases. For a low percentage of cases, the rigid body registration and position correction did not account for small changes in spine curvature. Simple adaptive correction schemes can be applied to further protect the cord without the need for the treatment plan re-optimization. Author Disclosure: A. Harrison, None; G. Bednarz, None; J.M. Galvin, None.
2875
An Automatic Learning Technique for Parameter Optimization in Inverse Planning
1
F. Stieler , H. Yan2, C. G. Willett2, F. Yin2 1
Universitaetsklinikum Mannheim, Mannheim, Germany, 2Duke University, Durham, NC
Purpose/Objective(s): Inverse planning often needs manual adjustment of parameters to compromise between target and critical organ doses. This process could be replaced by an automated approach such as fuzzy inference system (FIS) based on fuzzy logic. The fuzzy logic approach requires some pre-selected parameters based on prior knowledge. In order to optimize parameter selection in inverse planning of intensity-modulated radiation therapy, we are developing an automated learning technique called adaptive Neuro-Fuzzy Inference System (ANFIS). Materials/Methods: Conventionally, the model parameters of a FIS were manually pre-selected by human and then applied this pre-configured FIS on the parameter optimization of inverse planning. For making these model parameters optimal, a trial-anderror method was usually employed and an extended fine-tuning process of these model parameters is inevitable. For making this step automated and optimal, a new type of FIS, called ANFIS, is introduced and own the capability of learning model parameters from training samples. Whenever an input is presented to ANFIS, the output will be calculated and compared with the target of sample. The developed ANFIS system is interfaced with a commercial inverse planning system and examined with a real clinical case. Three inverse plans were generated using three different methods to optimize parameter selection by human planner, original FIS (manually specified model parameters), ANFIS, respectively. Results: Three strategies for training ANFIS were investigated. The ANFIS 1 was trained by 50 training and validation samples. The ANFIS 2 was trained by 100 training samples. The ANFIS 3 was trained by 100 training and validation samples. The all three ANFIS demonstrated the similar prediction errors. The results of a clinical case are presented in Fig. 1 to 3. The dose volume histograms (DVH) between plans conducted by original FIS and ANFIS were comparable as shown in Fig. 1. Compared with the plan made by human planner, the DVHs obtained from FIS plans were apparently improved. Dose sparing of rectum and bladder were observed while the similar dose coverage on PTV were maintained as shown in Fig. 2 and Fig. 3. Conclusions: The ANFIS demonstrated the capability of learning model parameters from training data. The preliminary clinical result indicated that the performance of ANFIS is comparable to those of the original FIS but no fine-tuning procedure of model parameters is necessary. As ANFIS makes the configuration of model parameters of FIS automated and optimal, it will greatly improve the efficiency of current parameter optimization procedure of inverse planning and save more time for planners on treatment planning. Author Disclosure: F. Stieler, None; H. Yan, None; C.G. Willett, None; F. Yin, None.
2876
Stick Around, Don’t Split: A Novel Approach to Reducing the Number of Split Fields in Large Field IMRT
1
C. C. Lee , A. Wu2, M. Garg1, S. Mutyala1, S. Kalnicki1, D. Mah1 1 Montefiore Medical Center, Bronx, NY, 2Department of Radiologic Sciences, Thomas Jefferson University, Philadelphia, PA Purpose/Objective(s): IMRT has been applied to head and neck, lung, pelvis and high-risk prostate cancer treatments. The design of the Varian MLC requires that fields with widths greater than 14 cm be split into two or more carriage movements. With the split
S691