Predicting dose‐volume histograms for organs‐at‐risk in IMRT planning

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.

Section: Introductionmentioning

confidence: 99%

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

Wang

Huang

et al. 2018

“…Quality control of the planned dose of radiation absorbed by OAR during radiotherapy is critically important 24, 25, 26, 27, 28, 29. In the present study, OAR for cervical carcinoma and NPC were divided into sOAR and a multiple linear regression analyses were used to fit the experimental data for sOAR volumes and gEUD ( a = 1).…”

Section: Discussionmentioning

confidence: 99%

A quality control method for intensity‐modulated radiation therapy planning based on generalized equivalent uniform dose

Pang

Sun

Yang

et al. 2018

To ensure good quality intensity‐modulated radiation therapy (IMRT) planning, we proposed the use of a quality control method based on generalized equivalent uniform dose (gEUD) that predicts absorbed radiation doses in organs at risk (OAR). We conducted a retrospective analysis of patients who underwent IMRT for the treatment of cervical carcinoma, nasopharyngeal carcinoma (NPC), or non‐small cell lung cancer (NSCLC). IMRT plans were randomly divided into data acquisition and data verification groups. OAR in the data acquisition group for cervical carcinoma and NPC were further classified as sub‐organs at risk (sOAR). The normalized volume of sOAR and normalized gEUD (a = 1) were analyzed using multiple linear regression to establish a fitting formula. For NSCLC, the normalized intersection volume of the planning target volume (PTV) and lung, the maximum diameter of the PTV (left–right, anterior–posterior, and superior–inferior), and the normalized gEUD (a = 1) were analyzed using multiple linear regression to establish a fitting formula for the lung gEUD (a = 1). The r‐squared and P values indicated that the fitting formula was a good fit. In the data verification group, IMRT plans verified the accuracy of the fitting formula, and compared the gEUD (a = 1) for each OAR between the subjective method and the gEUD‐based method. In conclusion, the gEUD‐based method can be used effectively for quality control and can reduce the influence of subjective factors on IMRT planning optimization.

“…Initially, conformal X jaws to PTV border without margin were placed by the API, larger than which may increase unnecessary OAR exposure from MLC dose leakage. The Y jaws were further retracted by the width of an adjacent leaf of Millennium 120 MLCs for scatter contribution 13. Keeping all other parameters unchanged, plans with various X jaw sizes and positions were created by the scripts: One patient was used to test the method feasibility and display the dosimetric sensitivity to finer jaw changes, where the X jaws were gradually extended toward the isocenter by 50 mm (5 mm/step, 10 steps for each bank).…”

Section: Methodsmentioning

confidence: 99%

Fully automated searching for the optimal VMAT jaw settings based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge‐based planning

Huang

Yue

Wang

et al. 2018

PurposeEclipse treatment planning system has not been able to optimize the jaw positions for Volumetric Modulated Arc Therapy (VMAT). The arbitrary and planner‐dependent jaw placements define the maximum field size within which multi‐leaf‐collimator (MLC) sequences can be optimized to modulate the beam. Considering the mechanical constraints of MLC transitional speed and range, suboptimal X jaw settings may impede the optimization or undermine the deliverability. This work searches optimal VMAT jaw settings automatically based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge‐based planning.Methods and materialsUsing an ESAPI script, the X jaws of rectal VMAT plans were initially set to conform the planning‐target‐volume (PTV), and were gradually extended toward the isocenter (PTV center) in 5–7 mm increments. Using these jaw pairs, 592 plans were automatically created for 10 patients and quantitatively evaluated using a comprehensive scoring function. A published RapidPlan model was evoked by ESAPI to generate patient‐specific optimization objectives without manual intervention. All candidate plans were first stored as text files to save storage space, and only the best, worst, and conformal plans were consequently recreated for comparison.ResultsAlthough RapidPlan estimates dose‐volume histogram (DVH) based on individual anatomy, the geometry‐based expected dose (GED) algorithm does not recognize different jaw settings but uses PTV‐conformal jaws as default; hence, identical DVHs were observed regardless of planner‐defined jaws. Therefore, ESAPI finalized dose‐volume calculation and eliminated the plans with unacceptable hotspots before comparison. The plan quality varied dramatically with different jaw settings. Trade‐offs among different organs‐at‐risk (OARs) were collectively considered by the proposed scoring method, which identified the best and worst plans correctly. The plans using conformal jaws were neither the best nor the worst of all candidates.Conclusions VMAT plans using optimal jaw locations can be created automatically using ESAPI and RapidPlan. Conformal jaws are not the optimal choice.