A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

Zarepisheh, Masoud; Long, Tammy M.; Li, Nan; Tian, Zhen; Romeijn, H. Edwin; Jia, Xun; Jiang, Steve B

doi:10.1118/1.4875700

Cited by 91 publications

(98 citation statements)

References 45 publications

(58 reference statements)

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“…Consistent with previous published findings 8 , 9 , 10 , 11 the automatic process of RapidPlan has largely improved the planning efficiency; based on our stand‐alone workstation (two processors of 2.00 GHz, 32.0 GB RAM, 64‐bit Windows 7 Ultimate system), a typical case could be finished in about 30 min without any interactive objective adjustment.…”

Section: Discussionsupporting

confidence: 85%

“…As reported by many inhouse approaches, knowledge‐based radiotherapy (KBRT) treatment planning is deemed to reduce the interplanner varieties of plan quality 1 , 2 , 3 , 4 , 5 , 6 , 7 and expedite the planning process 8 , 9 , 10 , 11 . As a commercial KBRT optimization engine, RapidPlan (Varian Medical Systems, Palo Alto, CA) uses a pool of selected plans with consistent high quality as historical knowledge to train a DVH estimation model which predicts achievable DVH ranges and acceptable trade‐offs during the semi‐automatic plan optimization for the prospective patient.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Yue

et al. 2016

J Applied Clin Med Phys

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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

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Section: Discussionsupporting

confidence: 85%

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

J Applied Clin Med Phys

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“…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

J Applied Clin Med Phys

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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.

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“…We would like to mention that a manual adjustment process was employed for the selection of the weighting factors in the optimization of the quadratic objective function, but this can be alleviated by incorporating the knowledge either from prior clinical experience 25,43,44 or from the data of previously treated patients. 45 In this case, all we need is an automated procedure to adjust the parameters in order to replicate prior clinical data.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous beam sampling and aperture shape optimization for SPORT

Zarepisheh

et al. 2015

Medical Physics

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Purpose: Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. Results: A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. Conclusions: The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans. C 2015 American Association of Physicists in Medicine. [http://dx

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A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

Cited by 91 publications

References 45 publications

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

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

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

Simultaneous beam sampling and aperture shape optimization for SPORT

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