- Email: [email protected]
Evaluation of a commercial automatic treatment planning system for prostate cancers
ARTICLE IN PRESS Medical Dosimetry ■■ (2017) ■■–■■
Medical Dosimetry j o u r n a l h o m e p a g e : w w w. m e d d o s . o r g
Dosimetry Contribution:
Evaluation of a commercial automatic treatment planning system for prostate cancers Kanabu Nawa, Ph.D.,* Akihiro Haga, Ph.D.,* Akihiro Nomoto, M.D.,* Raniel A. Sarmiento, M.Sc.,† Kenshiro Shiraishi, M.D., Ph.D.,‡ Hideomi Yamashita, M.D., Ph.D.,* and Keiichi Nakagawa, M.D., Ph.D.* *Department of Radiology, University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan; †Philips Radiation Oncology Systems, Fitchburg, WI; and ‡Department of Radiology, Teikyo University School of Medicine, Itabashi-ku, Tokyo, Japan
A R T I C L E
I N F O
Article history:
Received 25 November 2016 Received in revised form 1 March 2017 Accepted 29 March 2017 Keywords:
Automated planning optimization Prostate cancer Plan quality Interoperator variation
A B S T R A C T
Recent developments in Radiation Oncology treatment planning have led to the development of software packages that facilitate automated intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) planning. Such solutions include sitespecific modules, plan library methods, and algorithm-based methods. In this study, the plan quality for prostate cancer generated by the Auto-Planning module of the Pinnacle3 radiation therapy treatment planning system (v9.10, Fitchburg, WI) is retrospectively evaluated. The AutoPlanning module of Pinnacle3 uses a progressive optimization algorithm. Twenty-three prostate cancer cases, which had previously been planned and treated without lymph node irradiation, were replanned using the Auto-Planning module. Dose distributions were statistically compared with those of manual planning by the paired t-test at 5% significance level. AutoPlanning was performed without any manual intervention. Planning target volume (PTV) dose and dose to rectum were comparable between Auto-Planning and manual planning. The former, however, significantly reduced the dose to the bladder and femurs. Regression analysis was performed to examine the correlation between volume overlap between bladder and PTV divided by the total bladder volume and resultant V70. The findings showed that manual planning typically exhibits a logistic way for dose constraint, whereas Auto-Planning shows a more linear tendency. By calculating the Akaike information criterion (AIC) to validate the statistical model, a reduction of interoperator variation in Auto-Planning was shown. We showed that, for prostate cancer, the Auto-Planning module provided plans that are better than or comparable with those of manual planning. By comparing our results with those previously reported for head and neck cancer treatment, we recommend the homogeneous plan quality generated by the Auto-Planning module, which exhibits less dependence on anatomic complexity. © 2017 American Association of Medical Dosimetrists.
PACS number: 87.55.de. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Reprint requests to Kanabu Nawa, Ph.D., Department of Radiology, University of Tokyo Hospital, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8655, Japan. E-mail: [email protected] http://dx.doi.org/10.1016/j.meddos.2017.03.004 0958-3947/Copyright © 2017 American Association of Medical Dosimetrists
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Introduction Treatment planning is time-consuming, and the quality of the outcome depends on the method followed by each planner.1,2 This has been a long-standing problem in radiation therapy. Plan optimization is performed so as to minimize the objective function.3 The optimization process can be schematically compared to a kinetic process of a ball rolling on a curved surface. In general, the objective function has a complicated structure. Therefore, it is very difficult to know a priori the location of the minimum of the corresponding curved surface.4 In most cases, that minimum tends to deviate from the planning goal composed by several clinical dose constraints. Redefining the objective function multiple times for the minimum point to be properly led to the clinical goal is complex and each additional optimization increases the total planning time. Furthermore, even within the goal, there exists a large variety of dose distributions.5 The criterion of when the optimization should be stopped depends on each planner’s judgment. In this sense, severe interoperator variation does exist regarding the final outcome. The use of automated planning with simple input as the clinical goal would decrease interoperator variability in modern radiation therapy.6 In January 2015, the Auto-Planning module was released with the Pinnacle3 treatment planning system (v9.10, Philips Medical Systems, Fitchburg, WI). Auto-Planning successfully automated the consecutive multiple sequence optimization process using progressive optimization.7 Each optimization sequence is followed by quantitative evaluation and fine-tuning of the objective function. The automated optimization process reduces the total time required to generate a treatment plan. Furthermore, the initial outcome generated by Auto-Planning satisfies most of the clinical goal, effectively reducing interoperator variation. The efficiency brought about by Auto-Planning seems apparent.8 Therefore, the quantitative evaluation of its plan quality in the dose distributions becomes an important subject. Auto-Planning generates several planning structures: rings, hot and cold spot regions of interest (ROIs), residual structures, and other special ROIs to spare organs at risk (OARs). These automatically generated structures allow Auto-Planning to better dose control in terms of target coverage and OARs sparing. In this study, we evaluated the plan quality of AutoPlanning for prostate cancer cases in comparison with clinically delivered manual planning. By examining the correlation between volume overlap between OARs and target and resultant doses, we quantitatively showed a reduction of interoperator variation in Auto-Planning. The comparative evaluation has already been performed for head and neck cancer treatment plans.8-10 The prostate region and the head and neck regions have different anatomic complexities. By
comparing our results with those for head and neck, we report a homogeneous plan quality generated by the AutoPlanning module, with less dependence on anatomic complexity. Methods and Materials To evaluate the plan quality of Auto-Planning, we replanned 23 previously delivered clinical prostate IMRT treatment plans. The gross tumor volume (GTV) was equal to the intact prostate. The clinical target volume was equal to (1) GTV for the low-risk group, (2) GTV and the basal part of the seminal vesicle for the intermediate-risk group, and (3) GTV and the whole part of the seminal vesicle for the high-risk group. The 23 patients were randomly chosen irrespective of the risk factors. Accordingly, there were 2 patients in the low-risk group, 11 in the intermediate-risk group, and 10 in the high-risk group. Patients in the lowrisk group were mainly treated by surgery in the University of Tokyo Hospital. Therefore, the number in the low-risk group became less than those in other groups by randomly sampling the patient data from the radiation therapy department database. The planning target volume (PTV) consisted of the clinical target volume with a setup margin of 4 mm in the posterior direction and 5 mm in all other directions. The prescription dose for PTV was set to 76 Gy in 38 treatment fractions, with a coverage that dose to 95% of PTV was equal to 76 Gy by following References 11 and 12. All plans were delivered using single-beam VMAT on a Synergy system equipped with the Agility multileaf collimator (Elekta AB, Stockholm, Sweden). Each of the manual plans was created by a planner who was randomly selected from 6 medical physicists. Each plan strictly followed the University of Tokyo Hospital guidelines imposing the following clinical dose constraints: V40 < 60%, V65 < 30%, and V70 < 15% for rectum and bladder and Dmax < 55 Gy for femurs, taking into account the research result on a late toxicity after IMRT for prostate cancers.12 These plans were clinically delivered within 1.5 years before this study. Regarding the Auto-Planning module, the beam parameters and optimization goals formulated by dose constraints can be prepared as a protocol in the “Treatment Technique” interface. We compiled an appropriate single protocol from iterative test runs for 3 pilot patients (1 in intermediate risk and 2 in high risk), and applied it to the 23 patients of our study (see Appendix A for the adopted optimization goal). The initial results of the Auto-Planning module were not followed by further manual intervention. All of the delineations of PTV and OARs as well as the position of the isocenter of each plan were shared between Auto-Planning and manual planning. To compare the results between AutoPlanning and manual planning, we performed a paired t-test at 5% significance level.
ARTICLE IN PRESS K. Nawa et al. / Medical Dosimetry ■■ (2017) ■■–■■
The Auto-Planning module creates lots of special ROIs to generate a high-quality dose distribution (see Appendix B). It is very difficult to manually create such special ROIs for all plans, so that, practically, the creation of additive ROIs is constrictive and depends on the effort of each manual planner. In this sense, by using the Auto-Planning module, quantitatively better plans could be generated relative to those of manual planning.
Results We performed the paired t-test for dose coverage of PTV between manual planning and Auto-Planning (see Fig. 1). Dose coverage was evaluated by the homogeneity index (CHI) and the conformity index (CCI), which are defined as:
CHI = Dmax Dmin, CCI = VDmin VPTV , where Dmax (Gy) and Dmin (Gy) are maximum and minimum PTV doses, VDmin (cc) is the volume where the dose is above Dmin, and VPTV (cc) is the volume of PTV. As CHI (CCI) approaches unity, a better PTV dose homogeneity (conformity) is realized. In 8 out of 23 plans generated by Auto-Planning, we observed the appearance of a hot spot corresponding to a dose more than 107% of the prescription dose of 76 Gy. For these 8 plans, the values of V107% (%) of PTV were 0.01, 0.01, 0.02, 0.02, 0.08, 0.30, 0.36, and 0.51 in ascending order. Because of such hot spots, Auto-Planning shows significantly inferior homogeneity as shown in Fig. 1A. On the other hand, Auto-Planning shows significantly superior conformity as shown in Fig. 1B. Generally, homogeneity and conformity compete with each other. Therefore, by loosening the constraint for conformity in Auto-Planning, some improvement for homogeneity can be expected. Moreover, sufficient conformity implies that the hot spot lies within the PTV, so that a small hot spot area can be acceptable if
3
sufficient conformity is achieved. As a whole, we recognized that manual planning and Auto-Planning showed almost comparable clinical quality for PTV dose. We also performed the paired t-test for doses to OARs, that is, bladder, rectum, and femur, between manual planning and Auto-Planning (see Fig. 2). We found that V40, V65, and V70 of the bladder (see Fig. 2A, B, and C) as well as the maximum femur dose (see Fig. 2G and H) in Auto-Planning were significantly smaller relative to those of manual planning. As for V40, V65, and V70 of the rectum, we could not find a significant difference between manual planning and Auto-Planning (see Fig. 2D, E, and F). Auto-Planning had outliers in V40, V65, and V70 of the bladder, indicated by arrows in Fig. 2A, B, and C. Among the 23 plans in Auto-Planning, we found only 1 that did not satisfy the dose constraint for bladder of V70 < 15.0%. To consider the mechanism of appearance of an outlier above the dose constraint, we examined by regression analysis the correlation between volume overlap between bladder and PTV divided by the total bladder volume and resultant V70 (see Fig. 3). We employed both linear and logistic regressions using Akaike information criterion (AIC) for validation.13 Regarding manual planning, we found an AIC value of 86.63 for linear and 81.15 for logistic regression. For Auto-Planning, we found an AIC value of 68.11 for linear and 71.47 for logistic regression. First, the small value of AIC implies that the behavior of the data can be well described by the corresponding statistical model. In this sense, the smaller value of AIC in Auto-Planning can be an evidence of reduction of interoperator variation. Second, the logistic behavior observed in manual planning is shown in Fig. 3A. That logistic behavior within the data could be explained in terms of the potential concern of the manual planner for the dose constraint V70 < 15.0% as well as for a possible second cancer due to low-dose radiation. On the other hand, the linear behavior in Auto-Planning is shown in Fig. 3B. Because of the
Fig. 1. Box-and-whisker plot for manual planning and Auto-Planning of the homogeneity index (A) and the conformity index (B). p-Values of the paired t-test between manual planning and Auto-Planning are shown in each figure.
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Fig. 2. Box-and-whisker plot for manual planning and Auto-Planning of V40 (A), V65 (B), and V70 (C) of the bladder; V40 (D), V65 (E), and V70 (F) of the rectum, and maximum dose of the left (G) and the right (H) femur. The outliners above dose constraint are indicated by an arrow in A, B, and C. p-Values of the paired t-test between manual planning and Auto-Planning are shown in each figure.
linearity, the optimization was not adapted to the accidental case where an overlap between bladder and PTV was significant. Thus, the outlier above dose constraint was outputted. In the Auto-Planning module, manual reoptimization with additive modification of the objective
function can be performed to modify the outcome. By such manual intervention, a logistic judgment for dose constraints will eventually be developed. By using the result of the Auto-Planning as the starting point, a high-quality plan can be realized with less interoperator variation.
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Fig. 3. Regression analysis for the correlation between overlap volume between bladder and PTV divided by bladder volume and resultant V70 for manual planning (A) and Auto-Planning (B). The result of linear (logistic) regression model is shown by solid (dashed) lines along with the corresponding AIC value in parentheses. The dotted horizontal lines show the clinical constraint of V70 < 15.0%.
Discussion We found that, for prostate cancer treatment planning, Auto-Planning gave us comparable or better plans relative to those of manual planning. On the other hand, for head and neck cancer treatment planning, Auto-Planning produces significantly better plans relative to those of manual planning with respect to most of OARs, for example, parotid gland, submandibular, and spinal cord, which were observed for any dose level of the dose-volume histogram.9 The treatment planning for prostate cancer has anatomic complexity that is different from the one for head and neck. In general, the plan quality generated by manual operation tends to decrease with increasing anatomic complexity. On the other hand, the automation implemented in AutoPlanning could generate a homogeneous outcome quality with less dependence on anatomic complexity. The improvement in plan quality using Auto-Planning was more evident on complex anatomic regions such as head and neck when compared with those of manual planning. As for the relationship between Auto-Planning and databased planning, one may expect a strong congruity. At present, the optimization goal in Auto-Planning is set by the planner from a clinical point of view. The clinical goal in “tailormade planning” depends on each patient’s particular case and derives from well-established databases.14 In future, a hybrid planning module could be realized where the optimization goal is set by the database, whereas optimization is led up to machinery limit by the Auto-Planning engine.
in Auto-Planning. By comparing our results with those for head and neck cancer treatment, we recommend the homogeneous outcome quality of Auto-Planning, which exhibits less dependence on anatomic complexity. Wide application of Auto-Planning for complex planning, for example, with multimetastasis around several OARs, is a future prospect. Appendices Appendix A. Optimization goal in “Treatment Technique” The optimization goal of Auto-Planning can be saved as a protocol in the “Treatment Technique” interface. It is not necessary that it coincides with the clinical goal. Practically, the optimization goal is determined by iterative trials for several pilot patients to numerically satisfy the clinical goal. AutoPlanning’s optimization process consists of a finite number of consecutive loops for progressive optimization. Each loop is constructed by usual optimization, quantitative evaluation, and fine-tuning of objective functions. Because of the finiteness of the number of loops, the harder the clinical goal to be satisfied, the severer should be the optimization goal of Auto-Planning relative to the clinical goal. Thus, the optimization goal is set in advance relative to the clinical goal.
Table 1 OAR optimization goals in “Treatment Technique” OAR
Optimization goal
Priority
Rectum
Dmax < 70 Gy Dmean < 7 Gy V70 < 10% V40 < 30% Dmax < 70 Gy Dmean < 25 Gy V70 < 10% V40 < 40% Dmax < 35 Gy
High High High High Medium Medium Medium Medium Medium
Conclusion In this study, we evaluated the plan quality for prostate cancer treatment of Auto-Planning relative to manual planning. We found that Auto-Planning provided comparable or better plans relative to those of manual planning. Regression analysis suggests a reduction of interoperator variation
Bladder
Femur
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Fig. 4. Schematic figure of standard ROIs (A). Two targets, PTV70 and PTV60, with respective prescription doses of 70 Gy and 60 Gy, exist. OAR1 is a serial organ, whereas OAR2 is a parallel organ. Body ROI is not necessarily delineated as input. Schematic figure of ROIs created by Auto-Planning (B). The roles of other ROIs are explained in the text.
Here, we outline the adopted optimization goal for this study, determined by iterative trials for 3 pilot patients. We prepared 2 targets: “planning target volume (PTV)” and “PTV + 1 mm.” The latter consists of PTV with a 1-mm margin having the role of providing sufficient dose coverage for PTV. A prescription dose of 76 Gy is set for both of the targets in the target optimization goal. The organs at risk (OARs) optimization goals are summarized in Table 1. Users can determine the priority for each goal by 3 levels: low, medium, and high. The priority is automatically adjusted by analyzing the overlap between OARs and the respective target(s). If the OAR has a large overlap with the target(s), the priority is automatically lowered. Such automatic adjustment of priority can provide flexibility for the variety of patient cases, and, eventually, minimize patientspecific settings. In this study, we chose high priority for the rectum to positively spare it relative to the other OARs. One can also set the details of optimization in the “Advanced Settings” interface. Tuning balance (%) adjusts overall balance between OAR sparing and target coverage. It takes values between 0 and 100, and the target coverage is prioritized to OAR sparing as the tuning balance approaches zero. Dose falloff margin (cm) controls the thickness of the ring region of interest (ROI) around the targets (see Appendix B). Hot spot maximum goal (%) specifies the value of dose to associate with a hot spot region. We chose 10 (%) and 2.5 (cm) for tuning balance and dose falloff margin, respectively. We also chose the relatively small value of 103 (%) for the hot spot maximum goal so as to suppress the occurrence of hot spots.
dard ROIs, including target and OAR names as input, a list of additive ROIs is automatically created as follows: (1) “Resd_(OAR name)_AP”: compromised, that is, parallel OAR structure where overlaps with targets are removed. (2) “(Target name)_AP”: target structure where overlaps with non-compromised, that is, serial OARs and also other targets having larger prescription dose, are removed. (3) “TargetSurround_AP”: ring structure around the sum of all of (Target_name)_AP. The dose falloff margin (see Appendix A) is denoted as M DFO ; the thickness of TargetSurround_AP is 0.8 × MDFO and its distance from the surface of (Target_name)_AP is 0.2 × MDFO. (4) “BodyMinusTarget_AP”: structure filling between body surface and TargetSurround_AP. Body surface is automatically recognized by the computed tomography image, so that manual delineation of body ROI is not necessary. (5) “(Target name)_AP_HS#” and “(Target name)_AP_CS#”: hot spot and cold spot structures to control homogeneity of target dose distribution. The “#” indicates the value of prescription dose of the target and the number of optimization loop in which it is created during the progressive optimization process. (6) “(OAR name)_EN_AP”: structure enforcing optimization to satisfy maximum dose goals of OARs. A schematic figure of these additive ROIs is shown in Fig. 4. References
Appendix B. Automatic ROI creation To generate a high-quality dose distribution, the AutoPlanning module creates various additive ROIs in the consecutive loops of the optimization process. With stan-
1. Nelms, B.E.; Robinson, G.; Markham, J.; et al. Variation in external beam treatment plan quality: an inter-institutional study of planners and planning systems. Pract. Radiat. Oncol. 2:296–305; 2012. 2. Batumalai, V.; Jameson, M.G.; Forstner, D.F.; et al. How important is dosimetrist experience for intensity modulated radiation therapy?
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A comparative analysis of a head and neck case. Pract. Radiat. Oncol. 3(3):99–e106; 2013. Löf, J.; Rehbinder, H. Inverse Planning Optimization with RayOptimizer® in Pinnacle3®. Stockholm: RaySearch Laboratories; 2002. Intensity Modulated Radiation Therapy Collaborative Working Group. Intensity-modulated radiotherapy: current status and issues of interest. Int. J. Radiat. Oncol. Biol. Phys. 51(4):880–914; 2001. Moore, K.L.; Brame, R.S.; Low, D.A.; et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int. J. Radiat. Oncol. Biol. Phys. 81:545–51; 2011. Moore, K.L.; Kagadis, G.C.; McNutt, T.R.; et al. Vision 20/20: automation and advanced computing in clinical radiation oncology. Med. Phys. 41(1):010901; 2014. Xhaferllari, I.; Wong, E.; Bzdusek, K.; et al. Automated IMRT planning with regional optimization using planning scripts. J. Appl. Clin. Med. Phys. 14(1):176–91; 2013. Krayenbuehl, J.; Norton, I.; Studer, G.; et al. Evaluation of an automated knowledge based treatment planning system for head and neck. Radiat. Oncol. 10:226; 2015.
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9. Hazell, I.; Bzdusek, K.; Kumar, P.; et al. Automatic planning of head and neck treatment plans. J. Appl. Clin. Med. Phys. 17(1): 2016. 10. Gintz, D.; Latifi, K.; Caudell, J.; et al. Initial evaluation of automated treatment planning software. J. Appl. Clin. Med. Phys. 17(3):2016. 11. ICRU. International Commission on Radiation Units and Measurements. Prescribing, recording, and reporting photon-beam intensity-modulated radiation therapy (IMRT). ICRU Report 83. J. ICRU 10:1–106; 2010. 12. Pederson, A.W.; Fricano, J.; Correa, D.; et al. Late toxicity after intensity-modulated radiation therapy for localized prostate cancer: an exploration of dose-volume histogram parameters to limit genitourinary and gastrointestinal toxicity. Int. J. Radiat. Oncol. Biol. Phys. 82(1):235–41; 2012. 13. Akaike, H. Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N.; Csaki, F.; eds. Second International Symposium on Information Theory. Budapest: Academiai Kiado. 267–81; 1973. 14. Naqa, I.E. Biomedical informatics and panomics for evidence-based radiation therapy. WIRES Data Min. Knowl. 4:327–40; 2014.
Medical Dosimetry j o u r n a l h o m e p a g e : w w w. m e d d o s . o r g
Dosimetry Contribution:
Evaluation of a commercial automatic treatment planning system for prostate cancers Kanabu Nawa, Ph.D.,* Akihiro Haga, Ph.D.,* Akihiro Nomoto, M.D.,* Raniel A. Sarmiento, M.Sc.,† Kenshiro Shiraishi, M.D., Ph.D.,‡ Hideomi Yamashita, M.D., Ph.D.,* and Keiichi Nakagawa, M.D., Ph.D.* *Department of Radiology, University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan; †Philips Radiation Oncology Systems, Fitchburg, WI; and ‡Department of Radiology, Teikyo University School of Medicine, Itabashi-ku, Tokyo, Japan
A R T I C L E
I N F O
Article history:
Received 25 November 2016 Received in revised form 1 March 2017 Accepted 29 March 2017 Keywords:
Automated planning optimization Prostate cancer Plan quality Interoperator variation
A B S T R A C T
Recent developments in Radiation Oncology treatment planning have led to the development of software packages that facilitate automated intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) planning. Such solutions include sitespecific modules, plan library methods, and algorithm-based methods. In this study, the plan quality for prostate cancer generated by the Auto-Planning module of the Pinnacle3 radiation therapy treatment planning system (v9.10, Fitchburg, WI) is retrospectively evaluated. The AutoPlanning module of Pinnacle3 uses a progressive optimization algorithm. Twenty-three prostate cancer cases, which had previously been planned and treated without lymph node irradiation, were replanned using the Auto-Planning module. Dose distributions were statistically compared with those of manual planning by the paired t-test at 5% significance level. AutoPlanning was performed without any manual intervention. Planning target volume (PTV) dose and dose to rectum were comparable between Auto-Planning and manual planning. The former, however, significantly reduced the dose to the bladder and femurs. Regression analysis was performed to examine the correlation between volume overlap between bladder and PTV divided by the total bladder volume and resultant V70. The findings showed that manual planning typically exhibits a logistic way for dose constraint, whereas Auto-Planning shows a more linear tendency. By calculating the Akaike information criterion (AIC) to validate the statistical model, a reduction of interoperator variation in Auto-Planning was shown. We showed that, for prostate cancer, the Auto-Planning module provided plans that are better than or comparable with those of manual planning. By comparing our results with those previously reported for head and neck cancer treatment, we recommend the homogeneous plan quality generated by the Auto-Planning module, which exhibits less dependence on anatomic complexity. © 2017 American Association of Medical Dosimetrists.
PACS number: 87.55.de. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Reprint requests to Kanabu Nawa, Ph.D., Department of Radiology, University of Tokyo Hospital, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8655, Japan. E-mail: [email protected] http://dx.doi.org/10.1016/j.meddos.2017.03.004 0958-3947/Copyright © 2017 American Association of Medical Dosimetrists
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Introduction Treatment planning is time-consuming, and the quality of the outcome depends on the method followed by each planner.1,2 This has been a long-standing problem in radiation therapy. Plan optimization is performed so as to minimize the objective function.3 The optimization process can be schematically compared to a kinetic process of a ball rolling on a curved surface. In general, the objective function has a complicated structure. Therefore, it is very difficult to know a priori the location of the minimum of the corresponding curved surface.4 In most cases, that minimum tends to deviate from the planning goal composed by several clinical dose constraints. Redefining the objective function multiple times for the minimum point to be properly led to the clinical goal is complex and each additional optimization increases the total planning time. Furthermore, even within the goal, there exists a large variety of dose distributions.5 The criterion of when the optimization should be stopped depends on each planner’s judgment. In this sense, severe interoperator variation does exist regarding the final outcome. The use of automated planning with simple input as the clinical goal would decrease interoperator variability in modern radiation therapy.6 In January 2015, the Auto-Planning module was released with the Pinnacle3 treatment planning system (v9.10, Philips Medical Systems, Fitchburg, WI). Auto-Planning successfully automated the consecutive multiple sequence optimization process using progressive optimization.7 Each optimization sequence is followed by quantitative evaluation and fine-tuning of the objective function. The automated optimization process reduces the total time required to generate a treatment plan. Furthermore, the initial outcome generated by Auto-Planning satisfies most of the clinical goal, effectively reducing interoperator variation. The efficiency brought about by Auto-Planning seems apparent.8 Therefore, the quantitative evaluation of its plan quality in the dose distributions becomes an important subject. Auto-Planning generates several planning structures: rings, hot and cold spot regions of interest (ROIs), residual structures, and other special ROIs to spare organs at risk (OARs). These automatically generated structures allow Auto-Planning to better dose control in terms of target coverage and OARs sparing. In this study, we evaluated the plan quality of AutoPlanning for prostate cancer cases in comparison with clinically delivered manual planning. By examining the correlation between volume overlap between OARs and target and resultant doses, we quantitatively showed a reduction of interoperator variation in Auto-Planning. The comparative evaluation has already been performed for head and neck cancer treatment plans.8-10 The prostate region and the head and neck regions have different anatomic complexities. By
comparing our results with those for head and neck, we report a homogeneous plan quality generated by the AutoPlanning module, with less dependence on anatomic complexity. Methods and Materials To evaluate the plan quality of Auto-Planning, we replanned 23 previously delivered clinical prostate IMRT treatment plans. The gross tumor volume (GTV) was equal to the intact prostate. The clinical target volume was equal to (1) GTV for the low-risk group, (2) GTV and the basal part of the seminal vesicle for the intermediate-risk group, and (3) GTV and the whole part of the seminal vesicle for the high-risk group. The 23 patients were randomly chosen irrespective of the risk factors. Accordingly, there were 2 patients in the low-risk group, 11 in the intermediate-risk group, and 10 in the high-risk group. Patients in the lowrisk group were mainly treated by surgery in the University of Tokyo Hospital. Therefore, the number in the low-risk group became less than those in other groups by randomly sampling the patient data from the radiation therapy department database. The planning target volume (PTV) consisted of the clinical target volume with a setup margin of 4 mm in the posterior direction and 5 mm in all other directions. The prescription dose for PTV was set to 76 Gy in 38 treatment fractions, with a coverage that dose to 95% of PTV was equal to 76 Gy by following References 11 and 12. All plans were delivered using single-beam VMAT on a Synergy system equipped with the Agility multileaf collimator (Elekta AB, Stockholm, Sweden). Each of the manual plans was created by a planner who was randomly selected from 6 medical physicists. Each plan strictly followed the University of Tokyo Hospital guidelines imposing the following clinical dose constraints: V40 < 60%, V65 < 30%, and V70 < 15% for rectum and bladder and Dmax < 55 Gy for femurs, taking into account the research result on a late toxicity after IMRT for prostate cancers.12 These plans were clinically delivered within 1.5 years before this study. Regarding the Auto-Planning module, the beam parameters and optimization goals formulated by dose constraints can be prepared as a protocol in the “Treatment Technique” interface. We compiled an appropriate single protocol from iterative test runs for 3 pilot patients (1 in intermediate risk and 2 in high risk), and applied it to the 23 patients of our study (see Appendix A for the adopted optimization goal). The initial results of the Auto-Planning module were not followed by further manual intervention. All of the delineations of PTV and OARs as well as the position of the isocenter of each plan were shared between Auto-Planning and manual planning. To compare the results between AutoPlanning and manual planning, we performed a paired t-test at 5% significance level.
ARTICLE IN PRESS K. Nawa et al. / Medical Dosimetry ■■ (2017) ■■–■■
The Auto-Planning module creates lots of special ROIs to generate a high-quality dose distribution (see Appendix B). It is very difficult to manually create such special ROIs for all plans, so that, practically, the creation of additive ROIs is constrictive and depends on the effort of each manual planner. In this sense, by using the Auto-Planning module, quantitatively better plans could be generated relative to those of manual planning.
Results We performed the paired t-test for dose coverage of PTV between manual planning and Auto-Planning (see Fig. 1). Dose coverage was evaluated by the homogeneity index (CHI) and the conformity index (CCI), which are defined as:
CHI = Dmax Dmin, CCI = VDmin VPTV , where Dmax (Gy) and Dmin (Gy) are maximum and minimum PTV doses, VDmin (cc) is the volume where the dose is above Dmin, and VPTV (cc) is the volume of PTV. As CHI (CCI) approaches unity, a better PTV dose homogeneity (conformity) is realized. In 8 out of 23 plans generated by Auto-Planning, we observed the appearance of a hot spot corresponding to a dose more than 107% of the prescription dose of 76 Gy. For these 8 plans, the values of V107% (%) of PTV were 0.01, 0.01, 0.02, 0.02, 0.08, 0.30, 0.36, and 0.51 in ascending order. Because of such hot spots, Auto-Planning shows significantly inferior homogeneity as shown in Fig. 1A. On the other hand, Auto-Planning shows significantly superior conformity as shown in Fig. 1B. Generally, homogeneity and conformity compete with each other. Therefore, by loosening the constraint for conformity in Auto-Planning, some improvement for homogeneity can be expected. Moreover, sufficient conformity implies that the hot spot lies within the PTV, so that a small hot spot area can be acceptable if
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sufficient conformity is achieved. As a whole, we recognized that manual planning and Auto-Planning showed almost comparable clinical quality for PTV dose. We also performed the paired t-test for doses to OARs, that is, bladder, rectum, and femur, between manual planning and Auto-Planning (see Fig. 2). We found that V40, V65, and V70 of the bladder (see Fig. 2A, B, and C) as well as the maximum femur dose (see Fig. 2G and H) in Auto-Planning were significantly smaller relative to those of manual planning. As for V40, V65, and V70 of the rectum, we could not find a significant difference between manual planning and Auto-Planning (see Fig. 2D, E, and F). Auto-Planning had outliers in V40, V65, and V70 of the bladder, indicated by arrows in Fig. 2A, B, and C. Among the 23 plans in Auto-Planning, we found only 1 that did not satisfy the dose constraint for bladder of V70 < 15.0%. To consider the mechanism of appearance of an outlier above the dose constraint, we examined by regression analysis the correlation between volume overlap between bladder and PTV divided by the total bladder volume and resultant V70 (see Fig. 3). We employed both linear and logistic regressions using Akaike information criterion (AIC) for validation.13 Regarding manual planning, we found an AIC value of 86.63 for linear and 81.15 for logistic regression. For Auto-Planning, we found an AIC value of 68.11 for linear and 71.47 for logistic regression. First, the small value of AIC implies that the behavior of the data can be well described by the corresponding statistical model. In this sense, the smaller value of AIC in Auto-Planning can be an evidence of reduction of interoperator variation. Second, the logistic behavior observed in manual planning is shown in Fig. 3A. That logistic behavior within the data could be explained in terms of the potential concern of the manual planner for the dose constraint V70 < 15.0% as well as for a possible second cancer due to low-dose radiation. On the other hand, the linear behavior in Auto-Planning is shown in Fig. 3B. Because of the
Fig. 1. Box-and-whisker plot for manual planning and Auto-Planning of the homogeneity index (A) and the conformity index (B). p-Values of the paired t-test between manual planning and Auto-Planning are shown in each figure.
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Fig. 2. Box-and-whisker plot for manual planning and Auto-Planning of V40 (A), V65 (B), and V70 (C) of the bladder; V40 (D), V65 (E), and V70 (F) of the rectum, and maximum dose of the left (G) and the right (H) femur. The outliners above dose constraint are indicated by an arrow in A, B, and C. p-Values of the paired t-test between manual planning and Auto-Planning are shown in each figure.
linearity, the optimization was not adapted to the accidental case where an overlap between bladder and PTV was significant. Thus, the outlier above dose constraint was outputted. In the Auto-Planning module, manual reoptimization with additive modification of the objective
function can be performed to modify the outcome. By such manual intervention, a logistic judgment for dose constraints will eventually be developed. By using the result of the Auto-Planning as the starting point, a high-quality plan can be realized with less interoperator variation.
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Fig. 3. Regression analysis for the correlation between overlap volume between bladder and PTV divided by bladder volume and resultant V70 for manual planning (A) and Auto-Planning (B). The result of linear (logistic) regression model is shown by solid (dashed) lines along with the corresponding AIC value in parentheses. The dotted horizontal lines show the clinical constraint of V70 < 15.0%.
Discussion We found that, for prostate cancer treatment planning, Auto-Planning gave us comparable or better plans relative to those of manual planning. On the other hand, for head and neck cancer treatment planning, Auto-Planning produces significantly better plans relative to those of manual planning with respect to most of OARs, for example, parotid gland, submandibular, and spinal cord, which were observed for any dose level of the dose-volume histogram.9 The treatment planning for prostate cancer has anatomic complexity that is different from the one for head and neck. In general, the plan quality generated by manual operation tends to decrease with increasing anatomic complexity. On the other hand, the automation implemented in AutoPlanning could generate a homogeneous outcome quality with less dependence on anatomic complexity. The improvement in plan quality using Auto-Planning was more evident on complex anatomic regions such as head and neck when compared with those of manual planning. As for the relationship between Auto-Planning and databased planning, one may expect a strong congruity. At present, the optimization goal in Auto-Planning is set by the planner from a clinical point of view. The clinical goal in “tailormade planning” depends on each patient’s particular case and derives from well-established databases.14 In future, a hybrid planning module could be realized where the optimization goal is set by the database, whereas optimization is led up to machinery limit by the Auto-Planning engine.
in Auto-Planning. By comparing our results with those for head and neck cancer treatment, we recommend the homogeneous outcome quality of Auto-Planning, which exhibits less dependence on anatomic complexity. Wide application of Auto-Planning for complex planning, for example, with multimetastasis around several OARs, is a future prospect. Appendices Appendix A. Optimization goal in “Treatment Technique” The optimization goal of Auto-Planning can be saved as a protocol in the “Treatment Technique” interface. It is not necessary that it coincides with the clinical goal. Practically, the optimization goal is determined by iterative trials for several pilot patients to numerically satisfy the clinical goal. AutoPlanning’s optimization process consists of a finite number of consecutive loops for progressive optimization. Each loop is constructed by usual optimization, quantitative evaluation, and fine-tuning of objective functions. Because of the finiteness of the number of loops, the harder the clinical goal to be satisfied, the severer should be the optimization goal of Auto-Planning relative to the clinical goal. Thus, the optimization goal is set in advance relative to the clinical goal.
Table 1 OAR optimization goals in “Treatment Technique” OAR
Optimization goal
Priority
Rectum
Dmax < 70 Gy Dmean < 7 Gy V70 < 10% V40 < 30% Dmax < 70 Gy Dmean < 25 Gy V70 < 10% V40 < 40% Dmax < 35 Gy
High High High High Medium Medium Medium Medium Medium
Conclusion In this study, we evaluated the plan quality for prostate cancer treatment of Auto-Planning relative to manual planning. We found that Auto-Planning provided comparable or better plans relative to those of manual planning. Regression analysis suggests a reduction of interoperator variation
Bladder
Femur
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Fig. 4. Schematic figure of standard ROIs (A). Two targets, PTV70 and PTV60, with respective prescription doses of 70 Gy and 60 Gy, exist. OAR1 is a serial organ, whereas OAR2 is a parallel organ. Body ROI is not necessarily delineated as input. Schematic figure of ROIs created by Auto-Planning (B). The roles of other ROIs are explained in the text.
Here, we outline the adopted optimization goal for this study, determined by iterative trials for 3 pilot patients. We prepared 2 targets: “planning target volume (PTV)” and “PTV + 1 mm.” The latter consists of PTV with a 1-mm margin having the role of providing sufficient dose coverage for PTV. A prescription dose of 76 Gy is set for both of the targets in the target optimization goal. The organs at risk (OARs) optimization goals are summarized in Table 1. Users can determine the priority for each goal by 3 levels: low, medium, and high. The priority is automatically adjusted by analyzing the overlap between OARs and the respective target(s). If the OAR has a large overlap with the target(s), the priority is automatically lowered. Such automatic adjustment of priority can provide flexibility for the variety of patient cases, and, eventually, minimize patientspecific settings. In this study, we chose high priority for the rectum to positively spare it relative to the other OARs. One can also set the details of optimization in the “Advanced Settings” interface. Tuning balance (%) adjusts overall balance between OAR sparing and target coverage. It takes values between 0 and 100, and the target coverage is prioritized to OAR sparing as the tuning balance approaches zero. Dose falloff margin (cm) controls the thickness of the ring region of interest (ROI) around the targets (see Appendix B). Hot spot maximum goal (%) specifies the value of dose to associate with a hot spot region. We chose 10 (%) and 2.5 (cm) for tuning balance and dose falloff margin, respectively. We also chose the relatively small value of 103 (%) for the hot spot maximum goal so as to suppress the occurrence of hot spots.
dard ROIs, including target and OAR names as input, a list of additive ROIs is automatically created as follows: (1) “Resd_(OAR name)_AP”: compromised, that is, parallel OAR structure where overlaps with targets are removed. (2) “(Target name)_AP”: target structure where overlaps with non-compromised, that is, serial OARs and also other targets having larger prescription dose, are removed. (3) “TargetSurround_AP”: ring structure around the sum of all of (Target_name)_AP. The dose falloff margin (see Appendix A) is denoted as M DFO ; the thickness of TargetSurround_AP is 0.8 × MDFO and its distance from the surface of (Target_name)_AP is 0.2 × MDFO. (4) “BodyMinusTarget_AP”: structure filling between body surface and TargetSurround_AP. Body surface is automatically recognized by the computed tomography image, so that manual delineation of body ROI is not necessary. (5) “(Target name)_AP_HS#” and “(Target name)_AP_CS#”: hot spot and cold spot structures to control homogeneity of target dose distribution. The “#” indicates the value of prescription dose of the target and the number of optimization loop in which it is created during the progressive optimization process. (6) “(OAR name)_EN_AP”: structure enforcing optimization to satisfy maximum dose goals of OARs. A schematic figure of these additive ROIs is shown in Fig. 4. References
Appendix B. Automatic ROI creation To generate a high-quality dose distribution, the AutoPlanning module creates various additive ROIs in the consecutive loops of the optimization process. With stan-
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