Prog Med Phys.  2019 Dec;30(4):94-103. 10.14316/pmp.2019.30.4.94.

Volumetric-Modulated Arc Radiotherapy Using Knowledge-Based Planning: Application to Spine Stereotactic Body Radiotherapy

• Jeong C
• Park JW
• Kwak J
• Song SY
• Cho B

• KMID: 2468170
• DOI: http://doi.org/10.14316/pmp.2019.30.4.94

Abstract

PURPOSE
To evaluate the clinical feasibility of knowledge-based planning (KBP) for volumetric-modulated arc radiotherapy (VMAT) in spine stereotactic body radiotherapy (SBRT).
METHODS
Forty-eight VMAT plans for spine SBRT was studied. Two planning target volumes (PTVs) were defined for simultaneous integrated boost: PTV for boost (PTV-B: 27 Gy/3fractions) and PTV elective (PTV-E: 24 Gy/3fractions). The expert VMAT plans were manually generated by experienced planners. Twenty-six plans were used to train the KBP model using Varian RapidPlan. With the trained KBP model each KBP plan was automatically generated by an individual with little experience and compared with the expert plan (closed-loop validation). Twenty-two plans that had not been used for KBP model training were also compared with the KBP results (open-loop validation).
RESULTS
Although the minimal dose of PTV-B and PTV-E was lower and the maximal dose was higher than those of the expert plan, the difference was no larger than 0.7 Gy. In the closed-loop validation, D(1.2cc), D(0.35cc), and D(mean) of the spinal cord was decreased by 0.9 Gy, 0.6 Gy, and 0.9 Gy, respectively, in the KBP plans (P<0.05). In the open-loop validation, only D(mean) of the spinal cord was significantly decreased, by 0.5 Gy (P<0.05).
CONCLUSIONS
The dose coverage and uniformity for PTV was slightly worse in the KBP for spine SBRT while the dose to the spinal cord was reduced, but the differences were small. Thus, inexperienced planners could easily generate a clinically feasible plan for spine SBRT by using KBP.

Keyword

Radiotherapy; Intensity-modulated; Radiotherapy planning, computer-assisted; Machine learning; Radiosurgery

MeSH Terms

Clothing
Machine Learning
Radiosurgery*
Radiotherapy Planning, Computer-Assisted
Radiotherapy*
Spinal Cord
Spine*

Figure

• 

  Fig. 1 Closed-loop validation and open-loop validation. All 26 plans used for the training were re-optimized using the estimated dose-volume histogram (DVH) in the model (closed-loop validation). Another 22 plans that were not used for the model were also re-optimized using the model-predicted DVH (open-loop validation). These plans were all compared with the expert plans.

• 

  Fig. 2 A RapidPlan treatment plan window. (a) The dose-volume histogram (DVH) is estimated by the knowledge-based plan model considering a patient-specific geometric relationship between the target and the surrounding organs at risk (OARs). (b) The constraint objective for an OAR is generated using the estimated DVH. Priority can be given to each DVH objective, but the RapidPlan assigns a specific dose constraint to every volume point based on the estimated DVH, with equal weighting. (c) This constraint is expressed as a line in the DVH. Therefore, the upper constraint was used for each case, on an individual basis, because planning was difficult in a serial organ such as the spinal cord and esophagus.

• 

  Fig. 3 A representative case of spine stereotactic body radiotherapy planning. (a) Dose distribution. (b) Dose-volume histogram for the expert plan and the RapidPlan.

• 

  Fig. 4 Dose-volume histogram comparison between the expert plan (dotted line) and the RapidPlan (solid line). (a) Closed-loop validation. (b) Open-loop validation. PTV, planning target volume; PTV-E, PTV elective; PTV-B, PTV for boost.

• 

  Fig. 5 Dose-volume histogram comparison between the expert plan (dotted line) and the RapidPlan (solid line) with or without use of a virtual volume. (a) Plan with a virtual volume. (b) Plan without a virtual volume. PTV, planning target volume; PTV-E, PTV elective; PTV-B, PTV for boost.

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