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Title: MO-G-201-01: A Multi-Institutional Study Investigating the Performance of a Knowledge-Based Planning System Against Pinnacle Auto-Planning Engine in SIB-IMRT for the Head-And-Neck Cancer

Abstract

Purpose: Knowledge-based Planning (KBP) founded on prior planning experience and Auto-Planning Engine (APE; commercialized in Pinnacle v9.10 TPS) based on progressive optimization algorithm both aim to eliminate the trial-and-error process in radiotherapy inverse planning. This study investigates the performance of the approaches in a multi-institutional setting to evaluate their functionalities in oropharyngeal cancer and offers suggestions how they can be implemented in the clinic. Methods: Radboud University Medical Center (RUMC) provided 35 oropharyngeal cancer patients (SIB-IMRT with two-dose-level prescription: 68 Gy to PTV68 and 50.3 Gy to PTV50.3) with corresponding comparative APE plans. Johns Hopkins University (JHU) contributed to a three-dose-level (70 Gy 63 Gy and 58.1 Gy) plan library for RUMC’s patient KBP generation. MedStar Georgetown University Hospital (MGUH) contributed to a KBP approach employing overlap-volume histogram (OVH-KBP) for generating RUMC’s patient KBP plans using JHU’s plan library. Since both approaches need their own user-defined parameters as initial inputs the first 10 patients were set aside as training set to finalize them. Meanwhile cross-institutional comparisons and adjustments were implemented for investigating institutions’ protocol discrepancies and the approaches’ user-defined parameters were updated accordingly. The finalized parameters were then applied to the remaining 25 patients for OVH-KBP and APE generation. Amore » Wilcoxon rank-sum test was used for statistical comparison with significance level of p<0.05. Results: On average PTV68’s V95 was 96.5% in APE plans vs. 97% in OVH-KBP plans (p=0.36); PTV50.3’s V95 in APE plans was 97.8% vs.97.6% in OVH-KBP plans (p=0.6); cord’s D0.1 cc was 38.6 Gy in OVH-KBP plans vs. 43.7 Gy in APE plans (p=0.0001); mean doses to larynxes oral cavities parotids and submandibular glands were similar with p>0.2. Conclusions: The study demonstrates that KBP and APE can generate plans of comparable quality in a multi-institutional setting. Variations in clinical protocols can be effectively addressed for cross-institutional adaptations. Binbin Wu and Todd McNutt are the co-inventors of a patent associated with the proposed knowledge-based planning system which was licensed to Varian Medical Systems in 2015; This research was in part supported by Philips Radiation Oncology Systems.« less

Authors:
;  [1]; ; ;  [2];  [3];  [4]
  1. Georgetown University Hospital, Washington, DC (United States)
  2. Radboud University Medical Center, Nijmegen (Netherlands)
  3. Johns Hopkins University, Baltimore, MD (United States)
  4. Istituto Nazionale Tumori Regina Elena, Roma (Italy)
Publication Date:
OSTI Identifier:
22653910
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 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; 61 RADIATION PROTECTION AND DOSIMETRY; EDUCATIONAL FACILITIES; GENES; HEAD; NECK; NEOPLASMS; PATIENTS; PERFORMANCE; PLANNING; RADIOTHERAPY

Citation Formats

Wu, B, Pang, D, Kusters, M, Kunze-busch, M, Dijkema, T, McNutt, T, and Sanguineti, G. MO-G-201-01: A Multi-Institutional Study Investigating the Performance of a Knowledge-Based Planning System Against Pinnacle Auto-Planning Engine in SIB-IMRT for the Head-And-Neck Cancer. United States: N. p., 2016. Web. doi:10.1118/1.4957367.
Wu, B, Pang, D, Kusters, M, Kunze-busch, M, Dijkema, T, McNutt, T, & Sanguineti, G. MO-G-201-01: A Multi-Institutional Study Investigating the Performance of a Knowledge-Based Planning System Against Pinnacle Auto-Planning Engine in SIB-IMRT for the Head-And-Neck Cancer. United States. doi:10.1118/1.4957367.
Wu, B, Pang, D, Kusters, M, Kunze-busch, M, Dijkema, T, McNutt, T, and Sanguineti, G. 2016. "MO-G-201-01: A Multi-Institutional Study Investigating the Performance of a Knowledge-Based Planning System Against Pinnacle Auto-Planning Engine in SIB-IMRT for the Head-And-Neck Cancer". United States. doi:10.1118/1.4957367.
@article{osti_22653910,
title = {MO-G-201-01: A Multi-Institutional Study Investigating the Performance of a Knowledge-Based Planning System Against Pinnacle Auto-Planning Engine in SIB-IMRT for the Head-And-Neck Cancer},
author = {Wu, B and Pang, D and Kusters, M and Kunze-busch, M and Dijkema, T and McNutt, T and Sanguineti, G},
abstractNote = {Purpose: Knowledge-based Planning (KBP) founded on prior planning experience and Auto-Planning Engine (APE; commercialized in Pinnacle v9.10 TPS) based on progressive optimization algorithm both aim to eliminate the trial-and-error process in radiotherapy inverse planning. This study investigates the performance of the approaches in a multi-institutional setting to evaluate their functionalities in oropharyngeal cancer and offers suggestions how they can be implemented in the clinic. Methods: Radboud University Medical Center (RUMC) provided 35 oropharyngeal cancer patients (SIB-IMRT with two-dose-level prescription: 68 Gy to PTV68 and 50.3 Gy to PTV50.3) with corresponding comparative APE plans. Johns Hopkins University (JHU) contributed to a three-dose-level (70 Gy 63 Gy and 58.1 Gy) plan library for RUMC’s patient KBP generation. MedStar Georgetown University Hospital (MGUH) contributed to a KBP approach employing overlap-volume histogram (OVH-KBP) for generating RUMC’s patient KBP plans using JHU’s plan library. Since both approaches need their own user-defined parameters as initial inputs the first 10 patients were set aside as training set to finalize them. Meanwhile cross-institutional comparisons and adjustments were implemented for investigating institutions’ protocol discrepancies and the approaches’ user-defined parameters were updated accordingly. The finalized parameters were then applied to the remaining 25 patients for OVH-KBP and APE generation. A Wilcoxon rank-sum test was used for statistical comparison with significance level of p<0.05. Results: On average PTV68’s V95 was 96.5% in APE plans vs. 97% in OVH-KBP plans (p=0.36); PTV50.3’s V95 in APE plans was 97.8% vs.97.6% in OVH-KBP plans (p=0.6); cord’s D0.1 cc was 38.6 Gy in OVH-KBP plans vs. 43.7 Gy in APE plans (p=0.0001); mean doses to larynxes oral cavities parotids and submandibular glands were similar with p>0.2. Conclusions: The study demonstrates that KBP and APE can generate plans of comparable quality in a multi-institutional setting. Variations in clinical protocols can be effectively addressed for cross-institutional adaptations. Binbin Wu and Todd McNutt are the co-inventors of a patent associated with the proposed knowledge-based planning system which was licensed to Varian Medical Systems in 2015; This research was in part supported by Philips Radiation Oncology Systems.},
doi = {10.1118/1.4957367},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: HNC IMRT treatment planning is a challenging process that relies heavily on the planner’s experience. Previously, we used the single, best match from a library of manually planned cases to semi-automatically generate IMRT plans for a new patient. The current multi-case Knowledge Based Radiation Therapy (MC-KBRT) study utilized different matching cases for each of six individual organs-at-risk (OARs), then combined those six cases to create the new treatment plan. Methods: From a database of 103 patient plans created by experienced planners, MC-KBRT plans were created for 40 (17 unilateral and 23 bilateral) HNC “query” patients. For each case, 2Dmore » beam’s-eye-view images were used to find similar geometric “match” patients separately for each of 6 OARs. Dose distributions for each OAR from the 6 matching cases were combined and then warped to suit the query case’s geometry. The dose-volume constraints were used to create the new query treatment plan without the need for human decision-making throughout the IMRT optimization. The optimized MC-KBRT plans were compared against the clinically approved plans and Version 1 (original KBRT) using the dose metrics: mean, median, and maximum (brainstem and cord+5mm) doses. Results: Compared to Version 1, MC-KBRT had no significant reduction of the dose to any of the OARs in either unilateral/bilateral cases. Compared to the manually-planned unilateral cases, there was significant reduction of the oral cavity mean/median dose (>2Gy) at the expense of the contralateral parotid. Compared to the manually-planned bilateral cases, reduction of dose was significant in the ipsilateral parotid, larynx, and oral cavity (>3Gy mean/median) while maintaining PTV coverage. Conclusion: MC-KBRT planning in head and neck cancer generates IMRT plans with equivalent dose sparing to manually created plans. MC-KBRT using multiple case matches does not show significant dose reduction compared to using a single match case with dose warping.« less
  • Purpose: Knowledge Based Radiation Therapy Treatment (KBRT) planning can be used to semi-automatically generate IMRT plans for new patients using constraints derived from previously manually-planned, geometrically similar patients. We investigate whether KBRT plans can achieve greater dose sparing than manual plans using optimized, organspecific constraint weighting factors. Methods: KBRT planning of HNC radiotherapy cases geometrically matched each new (query) case to one of the 105 clinically approved plans in our database. The dose distribution of the planned match was morphed to fit the querys geometry. Dose-volume constraints extracted from the morphed dose distribution were used to run the IMRT optimizationmore » with no user input. In the first version, all constraints were multiplied by a weighting factor of 0.7. The weighting factors were then systematically optimized (in order of OARs with increasing separation from the target) to maximize sparing to each OAR without compromising other OARs. The optimized, second version plans were compared against the first version plans and the clinically approved plans for 45 unilateral/bilateral target cases using the dose metrics: mean, median and maximum (brainstem and cord) doses. Results: Compared to the first version, the second version significantly reduced mean/median contralateral parotid doses (>2Gy) for bilateral cases. Other changes between the two versions were not clinically meaningful. Compared to the original clinical plans, both bilateral and unilateral plans in the second version had lower average dose metrics for 5 of the 6 OARs. Compared to the original plans, the second version achieved dose sparing that was at least as good for all OARs and better for the ipsilateral parotid (bilateral) and oral cavity (bilateral/unilateral). Differences in planning target volume coverage metrics were not clinically significant. Conclusion: HNC-KBRT planning generated IMRT plans with at least equivalent dose sparing to manually generated plans; greater dose sparing was achieved in selected OARs.« less
  • Purpose: Volumetric intensity-modulated arc therapy (RA) allows for rapid delivery of highly conformal dose distributions. In this study, planning and dosimetry of RA were compared with conventional intensity-modulated radiation therapy (IMRT) plans of head-and-neck cancer patients. Materials and Methods: Computed tomography scans of 12 patients who had completed IMRT for advanced tumors of the naso-, oro- and hypopharynx were replanned using RA using either one or two arcs. Calculated doses to planning target volume (PTV) and organs at risk (OAR) were compared between IMRT and RA plans. Dose distributions for single arc (n = 8) and double arc (n =more » 4) plans were verified using film dosimetry in three to five coronal planes using a quality assurance phantom. Results: RA plans allowed for a mean reduction in number of monitor units (MU) by nearly 60%, relative to seven field sliding window IMRT plans. RA plans achieved similar sparing of all OAR as IMRT. Double arc RA provided the best dose homogeneity to PTV with a lower standard deviation of PTV dose (1.4 Gy), vs. single arc plans (2.0 Gy) and IMRT (1.7 Gy). Film measurements showed good correspondence with calculated doses; the mean gamma value was 0.30 (double arc) and area of the film with a gamma exceeding 1 was 0.82%. Conclusions: RA is a fast, safe, and accurate technique that uses lower MUs than conventional IMRT. Double arc plans provided at least similar sparing of OAR and better PTV dose homogeneity than single arc or IMRT.« less
  • Purpose: Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries. Methods and Materials: Volumetric modulated arc therapy plans of 60 recent head and neck cancer patients that included sparing of the salivary glands, swallowing muscles, and oral cavity were evenly divided between 2 models, Model{sub 30A} and Model{sub 30B}, and were combinedmore » in a third model, Model{sub 60}. Knowledge-based plans were created for 2 evaluation groups: evaluation group 1 (EG1), consisting of 15 recent patients, and evaluation group 2 (EG2), consisting of 15 older patients in whom only the salivary glands were spared. RapidPlan results were compared with clinical plans (CP) for boost and/or elective planning target volume homogeneity index, using HI{sub B}/HI{sub E} = 100 × (D2% − D98%)/D50%, and mean dose to composite salivary glands, swallowing muscles, and oral cavity (D{sub sal}, D{sub swal}, and D{sub oc}, respectively). Results: For EG1, RapidPlan improved HI{sub B} and HI{sub E} values compared with CP by 1.0% to 1.3% and 1.0% to 0.6%, respectively. Comparable D{sub sal} and D{sub swal} values were seen in Model{sub 30A}, Model{sub 30B}, and Model{sub 60}, decreasing by an average of 0.1, 1.0, and 0.8 Gy and 4.8, 3.7, and 4.4 Gy, respectively. However, differences were noted between individual organs at risk (OARs), with Model{sub 30B} increasing D{sub oc} by 0.1, 3.2, and 2.8 Gy compared with CP, Model{sub 30A}, and Model{sub 60}. Plan quality was less consistent when the patient was flagged as an outlier. For EG2, RapidPlan decreased D{sub sal} by 4.1 to 4.9 Gy on average, whereas HI{sub B} and HI{sub E} decreased by 1.1% to 1.5% and 2.3% to 1.9%, respectively. Conclusions: RapidPlan knowledge-based treatment plans were comparable to CP if the patient's OAR-planning target volume geometry was within the range of those included in the models. EG2 results showed that a model including swallowing-muscle and oral-cavity sparing can be applied to patients with only salivary gland sparing. This may allow model library sharing between institutes. Optimal detection of inadequate plans and population of model libraries requires further investigation.« less
  • Purpose: Prior work by the authors and other groups has studied the creation of automated intensity modulated radiotherapy (IMRT) plans of equivalent quality to those in a patient database of manually created clinical plans; those database plans provided guidance on the achievable sparing to organs-at-risk (OARs). However, in certain sites, such as head-and-neck, the clinical plans may not be sufficiently optimized because of anatomical complexity and clinical time constraints. This could lead to automated plans that suboptimally exploit OAR sparing. This work investigates a novel dose warping and scaling scheme that attempts to reduce effects of suboptimal sparing in clinicalmore » database plans, thus improving the quality of semiautomated head-and-neck cancer (HNC) plans. Methods: Knowledge-based radiotherapy (KBRT) plans for each of ten “query” patients were semiautomatically generated by identifying the most similar “match” patient in a database of 103 clinical manually created patient plans. The match patient’s plans were adapted to the query case by: (1) deforming the match beam fluences to suit the query target volume and (2) warping the match primary/boost dose distribution to suit the query geometry and using the warped distribution to generate query primary/boost optimization dose-volume constraints. Item (2) included a distance scaling factor to improve query OAR dose sparing with respect to the possibly suboptimal clinical match plan. To further compensate for a component plan of the match case (primary/boost) not optimally sparing OARs, the query dose volume constraints were reduced using a dose scaling factor to be the minimum from either (a) the warped component plan (primary or boost) dose distribution or (b) the warped total plan dose distribution (primary + boost) scaled in proportion to the ratio of component prescription dose to total prescription dose. The dose-volume constraints were used to plan the query case with no human intervention to adjust constraints during plan optimization. Results: KBRT and original clinical plans were dosimetrically equivalent for parotid glands (mean/median doses), spinal cord, and brainstem (maximum doses). KBRT plans significantly reduced larynx median doses (21.5 ± 6.6 Gy to 17.9 ± 3.9 Gy), and oral cavity mean (32.3 ± 6.2 Gy to 28.9 ± 5.4 Gy) and median (28.7 ± 5.7 Gy to 23.2 ± 5.3 Gy) doses. Doses to ipsilateral parotid gland, larynx, oral cavity, and brainstem were lower or equivalent in the KBRT plans for the majority of cases. By contrast, KBRT plans generated without the dose warping and dose scaling steps were not significantly different from the clinical plans. Conclusions: Fast, semiautomatically generated HNC IMRT plans adapted from existing plans in a clinical database can be of equivalent or better quality than manually created plans. The reductions in OAR doses in the semiautomated plans, compared to the clinical plans, indicate that the proposed dose warping and scaling method shows promise in mitigating the impact of suboptimal clinical plans.« less