Fully Automated Simultaneous Integrated Boosted–Intensity Modulated Radiation Therapy Treatment Planning Is Feasible for Head-and-Neck Cancer: A Prospective Clinical Study

Wu, Binbin; McNutt, Todd; Zahurak, Marianna; Simari, Patricio; Pang, Dalong; Taylor, Russell H.; Sanguineti, Giuseppe

doi:10.1016/j.ijrobp.2012.06.047

Cited by 87 publications

(81 citation statements)

References 17 publications

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“…1) and OAR (Fig. 2) metrics, the reduced degree of data dispersion in the RapidPlan group demonstrated its superior quality consistency and less variety than the original manual plans, which agreed with the RapidPlan rationale of alleviating the subjective dependency of individual planners 1 , 2 , 3 , 4 , 5 , 6 , 7 …”

Section: Discussionsupporting

confidence: 65%

“…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: 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

J Applied Clin Med Phys

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“…The same holds true for labor and computing resources, which can affect the implementation of new treatment planning techniques and treatment planning capacity. Various solutions are being investigated to improve planning consistency (3)(4)(5)(6)(7), including increased automation of planning by using knowledge-based approaches (8)(9)(10)(11)(12)(13). These approaches typically use libraries of existing patient plans to create models that predict the amount of organ-at-risk (OAR) sparing that can be achieved for a new patient, based, for example, on planning target volume (PTV)-OAR distance and overlap (14).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer

Tol

Delaney

Dahele

et al. 2015

International Journal of Radiation Oncology*Biology*Physics

245

253

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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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Fully Automated Simultaneous Integrated Boosted–Intensity Modulated Radiation Therapy Treatment Planning Is Feasible for Head-and-Neck Cancer: A Prospective Clinical Study

Cited by 87 publications

References 17 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

Evaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer

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

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