Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: A head-and-neck case study

RapidPlan, a commercial knowledge‐based optimizer, has been tested on head and neck, lung, esophageal, breast, liver, and prostate cancer patients. To appraise its performance on VMAT planning with simultaneous integrated boosting (SIB) for rectal cancer, this study configured a DVH (dose‐volume histogram) estimation model consisting 80 best‐effort manual cases of this type. Using the model‐ generated objectives, the MLC (multileaf collimator) sequences of other 70 clinically approved plans were reoptimized, while the remaining parameters, such as field geometry and photon energy, were maintained. Dosimetric outcomes were assessed by comparing homogeneity index (HI), conformal index (CI), hot spots (volumes receiving over 107% of the prescribed dose, normalV107%), mean dose and dose to the 50% volume of femoral head (normalDmean_FH and normalD50%_FH), and urinary bladder (normalDmean_UB and normalD50%_UB), and the mean DVH plotting. Paired samples t‐test or Wilcoxon signed‐rank test suggested that comparable CI were achieved by RapidPlan (0.99 ± 0.04 for PTVboost, and 1.03 ± 0.02 for PTV) and original plans (1.00 ± 0.05 for PTVboost and 1.03 ± 0.02 for PTV), respectively (p > 0.05). Slightly improved HI of planning target volume (PTVboost) and PTV were observed in the RapidPlan cases (0.05 ± 0.01 for PTVboost, and 0.26 ± 0.01 for PTV) than the original plans (0.06 ± 0.01 for PTVboost and 0.26 ± 0.01 for PTV), p < 0.05. More cases with positive V107% were found in the original (18 plans) than the RapidPlan group (none). RapidPlan significantly reduced the normalD50%_FH (by 1.53 Gy/9.86% from 15.52 ± 2.17 to 13.99 ± 1.16 Gy), normalDmean_FH (by 1.29 Gy/7.78% from 16.59±2.07 to 15.30±0.70 G), normalD50%_UB (by 4.93 Gy/17.50% from 28.17±3.07 to 23.24±2.13 Gy), and normalDmean_UB (by 3.94 Gy/13.43% from 29.34±2.34 to 25.40±1.36 Gy), respectively. The more concentrated distribution of RapidPlan data points indicated an enhanced consistency of plan quality.PACS number(s): 87.55.de; 87.55.dk

Section: Discussionsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

A dosimetric evaluation of knowledge‐based VMAT planning with simultaneous integrated boosting for rectal cancer patients

Jiang

Yue

et al. 2016

“…Knowledge‐based radiotherapy treatment planning is deemed to reduce the inter‐planner varieties of plan quality1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and expedite the planning process 14, 15, 16, 1718, 19 and displayed good compatibility across patient orientations, treatment techniques, and systems 20, 21…”

Section: Introductionmentioning

confidence: 99%

An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

Wang

Huang

et al. 2018

PurposeTo test if a RapidPlan DVH estimation model and its training plans can be improved interactively through a closed‐loop evolution process.Methods and materialsEighty‐one manual plans (P0) that were used to configure an initial rectal RapidPlan model (M0) were reoptimized using M0 (closed‐loop), yielding 81 P1 plans. The 75 improved P1 (P1+) and the remaining 6 P0 were used to configure model M1. The 81 training plans were reoptimized again using M1, producing 23 P2 plans that were superior to both their P0 and P1 forms (P2+). Hence, the knowledge base of model M2 composed of 6 P0, 52 P1+, and 23 P2+. Models were tested dosimetrically on 30 VMAT validation cases (Pv) that were not used for training, yielding Pv(M0), Pv(M1), and Pv(M2) respectively. The 30 Pv were also optimized by M2_new as trained by the library of M2 and 30 Pv(M0).ResultsBased on comparable target dose coverage, the first closed‐loop reoptimization significantly (P < 0.01) reduced the 81 training plans’ mean dose to femoral head, urinary bladder, and small bowel by 2.65 Gy/15.63%, 2.06 Gy/8.11%, and 1.47 Gy/6.31% respectively, which were further reduced significantly (P < 0.01) in the second closed‐loop reoptimization by 0.04 Gy/0.28%, 0.18 Gy/0.77%, 0.22 Gy/1.01% respectively. However, open‐loop VMAT validations displayed more complex and intertwined plan quality changes: mean dose to urinary bladder and small bowel decreased monotonically using M1 (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M2 (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M0. However, mean dose to femoral head increased by 0.81 Gy/6.64% (M1) and 0.91 Gy/7.46% (M2) than using M0. The overfitting problem was relieved by applying model M2_new.ConclusionsThe RapidPlan model and its constituent plans can improve each other interactively through a closed‐loop evolution process. Incorporating new patients into the original training library can improve the RapidPlan model and the upcoming plans interactively.

“…This process is along the line of our earlier work in automated weighting factors and model parameters determination 6 , 16 , 17 . Instead of simply using prior knowledge extracted from previous clinical treatment plan(s) as a “class solution” or as upper/lower bounds to examine the results of the optimization calculation, 18 , 19 , 20 , 21 , 22 , 23 , 24 in our approach, the reference information is utilized throughout the plan selection process. During the process, the parameters used in the Eclipse optimizer are constantly updated through the comparison of current and prior knowledge characterized by reference plans.…”

Section: Methodsmentioning

confidence: 99%

Application programming in C# environment with recorded user software interactions and its application in autopilot of VMAT/IMRT treatment planning

Wang

Liu

2016

An autopilot scheme of volumetric‐modulated arc therapy (VMAT)/intensity‐modulated radiation therapy (IMRT) planning with the guidance of prior knowledge is established with recorded interactions between a planner and a commercial treatment planning system (TPS). Microsoft (MS) Visual Studio Coded UI is applied to record some common planner‐TPS interactions as subroutines. The TPS used in this study is a Windows‐based Eclipse system. The interactions of our application program with Eclipse TPS are realized through a series of subroutines obtained by prerecording the mouse clicks or keyboard strokes of a planner in operating the TPS. A strategy to autopilot Eclipse VMAT/IMRT plan selection process is developed as a specific example of the proposed “scripting” method. The autopiloted planning is navigated by a decision function constructed with a reference plan that has the same prescription and similar anatomy with the case at hand. The calculation proceeds by alternating between the Eclipse optimization and the outer‐loop optimization independent of the Eclipse. In the C# program, the dosimetric characteristics of a reference treatment plan are used to assess and modify the Eclipse planning parameters and to guide the search for a clinically sensible treatment plan. The approach is applied to plan a head and neck (HN) VMAT case and a prostate IMRT case. Our study demonstrated the feasibility of application programming method in C# environment with recorded interactions of planner‐TPS. The process mimics a planner's planning process and automatically provides clinically sensible treatment plans that would otherwise require a large amount of manual trial and error of a planner. The proposed technique enables us to harness a commercial TPS by application programming via the use of recorded human computer interactions and provides an effective tool to greatly facilitate the treatment planning process.PACS number(s): 87.55.D‐, 87.55.kd, 87.55.de