The Erasmus MC Prostate Treatment Planning QA dataset, for evaluation of treatment planning QA models

Transcription

1 , for evaluation of treatment planning QA models Yibing Wang, Sebastiaan Breedveld, Ben Heijmen, Steven F. Petit* Department of Radiation Oncology - Erasmus MC Cancer Institute, Rotterdam, The Netherlands *s.petit@erasmusmc.nl Background and purpose: October 29, 2015 IMRT treatment planning with commercial treatment planning systems is a trial-and-error process, based on a series of subjective human decisions. Therefore the quality of the IMRT treatment plans may not be consistent among patients, planners or institutions with different experience. Different plan quality assurance (QA) models have been proposed recently, that could flag suboptimal plans that may benefit from an additional treatment planning effort [1-7]. However, in most studies the accuracy of the QA models was validated by comparing the predictions to treatment plans that were generated using the conventional trial-and-error approach as is used in clinical practice. This process may result in suboptimal plans and inconsistent tradeoffs between achieving PTV homogeneity and sparing different organs at risk (OARs). These plans are therefore not ideal to assess the accuracy of a treatment planning QA model. As a consequence it is far from straightforward to objectively and quantitatively assess and compare the prediction accuracy of different treatment planning QA models. Therefore we generated a dataset of Pareto-optimal IMRT plans for prostate cancer patients using Erasmus-iCycle. Erasmus-iCycle is an inhouse treatment planning system for fully automated, multi-criterial plan generation [8, 9]. The resulting dose distributions are Pareto optimal and reflect the different treatment planning tradeoffs in a consistent way among all patients. Therefore this dataset can be used as golden standard to assess the prediction accuracy of any plan QA methods. Materials and methods: 115 prostate IMRT plans were fully automatically generated using Erasmus-iCycle, with 23 equiangular beams as surrogate for VMAT plans [10]. The prescribed dose was 78 Gy to PTV1 (prostate) and 72.2 Gy to PTV2 PTV1 (seminal vesicles). The plans were generated fully automatically using a single wish list (shown in Table 1), which contains hard constraints and objectives in a predefined order of priority. The requirements of the hard constraints must be fulfilled, and the objectives were optimized as far as possible, in the predefined order of priority. The structure names are consistent among all patients. The dose distributions, treatment plans and contours are saved as DICOM RT objects RTDose, RTPlan and RTStructure.

2 Table 1: The wish list that is composed of hard constraints and prioritized objectives for plan generation LTCP = logarithmic tumor control probability, ensures homogeneous target coverage (see Appendix 1); geud = generalized equivalent uniform dose, D p1 = 78 Gy, D p2 = 72.2 Gy. Arrow down indicates minimization of that objective; PTV shell d mm: the dose constraint is applied to the surface of a shell at d mm from PTV2; External ring: The inner layer of the Patient structure, with the thickness of 20 mm; PTV1: planning target volume of the prostate; PTV2: Planning target volume of the prostate including seminal vesicles; PTV2 PTV1: The volume of PTV2 that is outside PTV1. Hard constraints Volume Type Limit PTV1 Maximum 104% D p1 PTV2 PTV1 Maximum 104% D p2 PTV2 shell at 50 mm Maximum 50% D p1 Rectum Maximum D p1 Bladder Maximum D p1 Femoral heads Maximum 40 Gy Unspecified tissue Maximum 104% D p1 Objectives Priority Volume Type Goal 1 PTV1 LTCP (α = 0.8) PTV2 LTCP (α = 0.8) Rectum geud (a = 12) 30 Gy 4 Rectum geud (a = 8) 20 Gy 5 Rectum mean 10 Gy 6 External ring maximum 40% D p1 7 PTV2 shell at 5 mm maximum 93% D p1 8 Anus mean 10 Gy 9 PTV2 shell at 15 mm maximum 70% D p1 10 PTV2 shell at 25 mm maximum 50% D p1 11 Bladder mean 40 Gy 12 Femoral heads maximum 20 Gy Results/conclusion: We have published a dataset of 115 Erasmus-iCycle treatment plans. The plans are Pareto optimal and reflect the different treatment planning tradeoffs in a consistent way among all patents. Therefore this dataset can be used as golden standard to assess the prediction accuracy of treatment planning QA models that use prior patient data to predict achievable dose metrics for new patients. The dataset has been made publicly available. See instructions for use. Instructions for use: The patient data is stored in four separate zip files of around 250 MB each named TreatmentPlanningQAReferencePlans_batch.zip 1 to 4 and can be downloaded here. Each file contains data of 28 or 29 patients stored in a separate zip files per patient. For each patient the DICOM RTStructure, the DICOM RTPlan and the DICOM RTDose are provided. Due to privacy constraints the CT

3 data could not be included. To use the data to assess the accuracy of a treatment planning QA model of interest, the patient data should be divided in a training and validation cohort. Next the planning QA model should be used to predict any dose metric of interest for the patients in the validation cohort. Differences between the predictions and achieved values for the patients in the validation cohort reflect the prediction accuracy of the treatment planning QA model. Please refer to the data as the Erasmus MC Prostate Treatment Planning QA dataset and add reference 8 of Breedveld et al. (see below) to any scientific writings that describe the use of this dataset. Appendix 1: LTCP The Logarithmic Tumor Control Probability (LTCP) cost function [11] ensures homogeneous target coverage, defined as: N LTCP = 1 N e α(d i D p ) i Where N is the number of voxels in the volume, D i is the dose to the i th voxel, D p is the prescribed dose and α is a predefined cell sensitivity parameter. The LTCP cost function controls PTV coverage. If the dose in each PTV voxel is equal to the prescribed dose, the LTCP equals 1. Voxels with a dose lower than the prescribed dose add to the penalty exponentially. If the dose is higher than the prescribed dose, then the LTCP value will slowly drop to 0. In our experience, the cell sensitivity parameter α = 0.8 results in acceptable coverage for the PTV. References [1] Wu, B, Ricchetti, F, Sanguineti, G, et al. Patient geometry-driven information retrieval for IMRT treatment plan quality control. Medical physics 2009;36: [2] Appenzoller, LM, Michalski, JM, Thorstad, WL, Mutic, S, Moore, KL. Predicting dose-volume histograms for organs-at-risk in IMRT planning. Medical physics 2012;39: [3] Good, D, Lo, J, Lee, WR, Wu, QJ, Yin, F-F, Das, SK. A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. International Journal of Radiation Oncology Biology Physics 2013;87: [4] Moore, KL, Brame, RS, Low, DA, Mutic, S. Experience-based quality control of clinical intensitymodulated radiotherapy planning. International Journal of Radiation Oncology Biology Physics 2011;81: [5] Zhu, X, Ge, Y, Li, T, Thongphiew, D, Yin, F-F, Wu, QJ. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Medical physics 2011;38: [6] Yuan, L, Ge, Y, Lee, WR, Yin, FF, Kirkpatrick, JP, Wu, QJ. Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans. Medical physics 2012;39: [7] Nwankwo, O, Sihono, DSK, Schneider, F, Wenz, F. A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites). Physics in medicine and biology 2014;59:5575.

4 [8] Breedveld, S, Storchi, PRM, Voet, PWJ, Heijmen, BJM. icycle: Integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans. Medical physics 2012;39: [9] Breedveld, S, Storchi, PRM, Heijmen, BJM. The equivalence of multi-criteria methods for radiotherapy plan optimization. Physics in medicine and biology 2009;54:7199. [10] Voet, PWJ, Dirkx, MLP, Breedveld, S, Al-Mamgani, A, Incrocci, L, Heijmen, BJM. Fully automated volumetric modulated arc therapy plan generation for prostate cancer patients. International Journal of Radiation Oncology Biology Physics 2014;88: [11] Alber, M, Reemtsen, R. Intensity modulated radiotherapy treatment planning by use of a barrier-penalty multiplier method. Optimisation Methods and Software 2007;22:

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