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Title: SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning

Abstract

Purpose: Prior knowledge-based treatment planning is impeded by the use of a single dose volume histogram (DVH) curve. Critical spatial information is lost from collapsing the dose distribution into a histogram. Even similar patients possess geometric variations that becomes inaccessible in the form of a single DVH. We propose a simple prior knowledge-based planning scheme that extracts features from prior dose distribution while still preserving the spatial information. Methods: A prior patient plan is not used as a mere starting point for a new patient but rather stopping criteria are constructed. Each structure from the prior patient is partitioned into multiple shells. For instance, the PTV is partitioned into an inner, middle, and outer shell. Prior dose statistics are then extracted for each shell and translated into the appropriate Dmin and Dmax parameters for the new patient. Results: The partitioned dose information from a prior case has been applied onto 14 2-D prostate cases. Using prior case yielded final DVHs that was comparable to manual planning, even though the DVH for the prior case was different from the DVH for the 14 cases. Solely using a single DVH for the entire organ was also performed for comparison but showed amore » much poorer performance. Different ways of translating the prior dose statistics into parameters for the new patient was also tested. Conclusion: Prior knowledge-based treatment planning need to salvage the spatial information without transforming the patients on a voxel to voxel basis. An efficient balance between the anatomy and dose domain is gained through partitioning the organs into multiple shells. The use of prior knowledge not only serves as a starting point for a new case but the information extracted from the partitioned shells are also translated into stopping criteria for the optimization problem at hand.« less

Authors:
;  [1]
  1. Stanford Univ School of Medicine, Stanford, CA (United States)
Publication Date:
OSTI Identifier:
22494091
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ANATOMY; OPTIMIZATION; PATIENTS; PERFORMANCE; PLANNING; PROSTATE; RADIATION DOSE DISTRIBUTIONS; RADIATION DOSES; RADIOTHERAPY

Citation Formats

Wang, H, and Xing, L. SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning. United States: N. p., 2015. Web. doi:10.1118/1.4924158.
Wang, H, & Xing, L. SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning. United States. doi:10.1118/1.4924158.
Wang, H, and Xing, L. Mon . "SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning". United States. doi:10.1118/1.4924158.
@article{osti_22494091,
title = {SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning},
author = {Wang, H and Xing, L},
abstractNote = {Purpose: Prior knowledge-based treatment planning is impeded by the use of a single dose volume histogram (DVH) curve. Critical spatial information is lost from collapsing the dose distribution into a histogram. Even similar patients possess geometric variations that becomes inaccessible in the form of a single DVH. We propose a simple prior knowledge-based planning scheme that extracts features from prior dose distribution while still preserving the spatial information. Methods: A prior patient plan is not used as a mere starting point for a new patient but rather stopping criteria are constructed. Each structure from the prior patient is partitioned into multiple shells. For instance, the PTV is partitioned into an inner, middle, and outer shell. Prior dose statistics are then extracted for each shell and translated into the appropriate Dmin and Dmax parameters for the new patient. Results: The partitioned dose information from a prior case has been applied onto 14 2-D prostate cases. Using prior case yielded final DVHs that was comparable to manual planning, even though the DVH for the prior case was different from the DVH for the 14 cases. Solely using a single DVH for the entire organ was also performed for comparison but showed a much poorer performance. Different ways of translating the prior dose statistics into parameters for the new patient was also tested. Conclusion: Prior knowledge-based treatment planning need to salvage the spatial information without transforming the patients on a voxel to voxel basis. An efficient balance between the anatomy and dose domain is gained through partitioning the organs into multiple shells. The use of prior knowledge not only serves as a starting point for a new case but the information extracted from the partitioned shells are also translated into stopping criteria for the optimization problem at hand.},
doi = {10.1118/1.4924158},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: The quality and efficiency of radiotherapy treatment planning are highly planer dependent. Previously we have developed a statistical model to correlate anatomical features with dosimetry features of head and neck Tomotherapy treatment. The model enables us to predict the best achievable dosimetry for individual patient prior to treatment planning. The purpose of this work is to study if the prediction model can facilitate the treatment planning in both the efficiency and dosimetric quality. Methods: The anatomy-dosimetry correlation model was used to calculate the expected DVH for nine patients formerly treated. In Group A (3 patients), the model prediction agreedmore » with the clinic plan; in Group B (3 patients), the model predicted lower larynx mean dose than the clinic plan; in Group C (3 patients), the model suggested the brainstem could be further spared. Guided by the prior knowledge, we re-planned all 9 cases. The number of interactions during the optimization process and dosimetric endpoints between the original clinical plan and model-guided re-plan were compared. Results: For Group A, the difference of target coverage and organs-at-risk sparing is insignificant (p>0.05) between the replan and the clinical plan. For Group B, the clinical plan larynx median dose is 49.4±4.7 Gy, while the prediction suggesting 40.0±6.2 Gy (p<0.05). The re-plan achieved 41.5±6.6 Gy, with similar dose on other structures as clinical plan. For Group C, the clinical plan brainstem maximum dose is 44.7±5.5 Gy. The model predicted lower value 32.2±3.8 Gy (p<0.05). The re-plans reduced brainstem maximum dose to 31.8±4.1 Gy without affecting the dosimetry of other structures. In the replanning of the 9 cases, the times operator interacted with TPS are reduced on average about 50% compared to the clinical plan. Conclusion: We have demonstrated that the prior expert knowledge embedded model improved the efficiency and quality of Tomotherapy treatment planning.« less
  • Purpose: Adaptive Radiotherapy (ART) with frequent CT imaging has been used to improve dosimetric accuracy by accounting for anatomical variations, such as primary tumor shrinkage and/or body weight loss, in Head and Neck (H&N) patients. In most ART strategies, the difference between the planned and the delivered dose is estimated by generating new plans on repeated CT scans using dose-volume constraints used with the initial planning CT without considering already delivered dose. The aim of this study was to assess the dosimetric gains achieved by re-planning based on prior dose by comparing them to re-planning not based-on prior dose formore » H&N patients. Methods: Ten locally-advanced H&N cancer patients were selected for this study. For each patient, six weekly CT imaging were acquired during the course of radiotherapy. PTVs, parotids, cord, brainstem, and esophagus were contoured on both planning and six weekly CT images. ART with weekly re-plans were done by two strategies: 1) Generating a new optimized IMRT plan without including prior dose from previous fractions (NoPriorDose) and 2) Generating a new optimized IMRT plan based on the prior dose given from previous fractions (PriorDose). Deformable image registration was used to accumulate the dose distributions between planning and six weekly CT scans. The differences in accumulated doses for both strategies were evaluated using the DVH constraints for all structures. Results: On average, the differences in accumulated doses for PTV1, PTV2 and PTV3 for NoPriorDose and PriorDose strategies were <2%. The differences in Dmean to the cord and brainstem were within 3%. The esophagus Dmean was reduced by 2% using PriorDose. PriorDose strategy, however, reduced the left parotid D50 and Dmean by 15% and 14% respectively. Conclusion: This study demonstrated significant parotid sparing, potentially reducing xerostomia, by using ART with IMRT optimization based on prior dose for weekly re-planning of H&N cancer patients.« less
  • Purpose: The study aims to develop and validate a knowledge based planning (KBP) model for external beam radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). Methods: RapidPlan™ technology was used to develop a lung KBP model. Plans from 65 patients with LA-NSCLC were used to train the model. 25 patients were treated with VMAT, and the other patients were treated with IMRT. Organs-at-risk (OARs) included right lung, left lung, heart, esophagus, and spinal cord. DVH and geometric distribution DVH were extracted from the treated plans. The model was trained using principal component analysis and step-wise multiple regression. Boxmore » plot and regression plot tools were used to identify geometric outliers and dosimetry outliers and help fine-tune the model. The validation was performed by (a) comparing predicted DVH boundaries to actual DVHs of 63 patients and (b) using an independent set of treatment planning data. Results: 63 out of 65 plans were included in the final KBP model with PTV volume ranging from 102.5cc to 1450.2cc. Total treatment dose prescription varied from 50Gy to 70Gy based on institutional guidelines. One patient was excluded due to geometric outlier where 2.18cc of spinal cord was included in PTV. The other patient was excluded due to dosimetric outlier where the dose sparing to spinal cord was heavily enforced in the clinical plan. Target volume, OAR volume, OAR overlap volume percentage to target, and OAR out-of-field volume were included in the trained model. Lungs and heart had two principal component scores of GEDVH, whereas spinal cord and esophagus had three in the final model. Predicted DVH band (mean ±1 standard deviation) represented 66.2±3.6% of all DVHs. Conclusion: A KBP model was developed and validated for radiotherapy of LA-NSCLC in a commercial treatment planning system. The clinical implementation may improve the consistency of IMRT/VMAT planning.« less
  • Purpose: The aim of this study was to investigate the dosimetric impact of the combination of photon energy and treatment technique on radiotherapy of localized prostate cancer when knowledge based planning was used. Methods: A total of 16 patients with localized prostate cancer were retrospectively retrieved from database and used for this study. For each patient, four types of treatment plans with different combinations of photon energy (6X and 10X) and treatment techniques (7-field IMRT and 2-arc VMAT) were created using a prostate DVH estimation model in RapidPlan™ and Eclipse treatment planning system (Varian Medical System). For any beam arrangement,more » DVH objectives and weighting priorities were generated based on the geometric relationship between the OAR and PTV. Photon optimization algorithm was used for plan optimization and AAA algorithm was used for final dose calculation. Plans were evaluated in terms of the pre-defined dosimetric endpoints for PTV, rectum, bladder, penile bulb, and femur heads. A Student’s paired t-test was used for statistical analysis and p > 0.05 was considered statistically significant. Results: For PTV, V95 was statistically similar among all four types of plans, though the mean dose of 10X plans was higher than that of 6X plans. VMAT plans showed higher heterogeneity index than IMRT plans. No statistically significant difference in dosimetry metrics was observed for rectum, bladder, and penile bulb among plan types. For left and right femur, VMAT plans had a higher mean dose than IMRT plans regardless of photon energy, whereas the maximum dose was similar. Conclusion: Overall, the dosimetric endpoints were similar regardless of photon energy and treatment techniques when knowledge based auto planning was used. Given the similarity in dosimetry metrics of rectum, bladder, and penile bulb, the genitourinary and gastrointestinal toxicities should be comparable among the selections of photon energy and treatment techniques.« less
  • Purpose: To reduce uncertainties in relative stopping power (RSP) estimates for ions (alpha and carbon) by using Ion radiographic-imaging and X-ray CT prior-knowledge. Methods: A 36×36 phantom matrix composed of 9 materials with different thicknesses and randomly placed is generated. Theoretical RSPs are calculated using stopping power (SP) data from three references (Janni, ICRU49 and Bischel). We introduced an artificial systematic error (1.5%, 2.5% or 3.5%) and a random error (<0.5%) to the SP to simulated patient ion-range errors present in clinic environment. Carbon/alpha final energy for each RSPs set (theoretical and from CT images) is obtained with a ray-tracingmore » algorithm. A gradient descent (GD) method is used to minimize the difference in exit particle energy, between theory and X-ray CT RSP maps, by iteratively correcting the RSP map from X-ray CT. Once a new set of RSPs is obtained for a direction a new optimization is done over other direction using the RSPs from the previous optimization. Theoretical RSPs are compared with experimental RSPs obtained with Gammex Phantom. Results: Preliminary results show that optimized RSP values can be obtained with smaller uncertainties (<1%) than clinical RSPs (1.5% to 3.5%). Theoretical values from three different references show uncertainties, up to 3% from experimental values. Further investigation will consider prior-knowledge from RSP obtained with CT images and ion radiographies from Monte Carlo Simulations. Conclusion: GD and ray-tracing methods have been implemented to reduce RSP uncertainties from values obtained for clinical treatment. Experimental RSPs will be obtained using carbon/alpha beams to consider the existence of material dependent systematic errors. Based on the results, it is hoped to show that using ray-tracing optimization with ion radiography and prior knowledge on RPSs, treatment planning accuracy and cost-effectiveness can be improved.« less