Efficiently train and validate a RapidPlan model through <scp>APQM</scp> scoring

Fusella, M.; Scaggion, A.; Pivato, N.; Rossato, M.A.; Zorz, Alessandra; Paiusco, M.

doi:10.1002/mp.12896

Cited by 28 publications

(20 citation statements)

References 15 publications

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“…Interesting is also the work of Fusella et al [37], who evaluated the plan quality of the cohort used to train the model with plan quality metrics, in order to select the plans with the highest dosimetric quality, reducing in part the plan variability of the data feeding the model.…”

Section: Discussionmentioning

confidence: 99%

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

et al. 2019

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PurposeTo determine if the performance of a knowledge based RapidPlan (RP) planning model could be improved with an iterative learning process, i.e. if plans generated by an RP model could be used as new input to re-train the model and achieve better performance.MethodsClinical VMAT plans from 83 patients presenting with head and neck cancer were selected to train an RP model, CL-1. With this model, new plans on the same patients were generated, and subsequently used as input to train a novel model, CL-2. Both models were validated on a cohort of 20 patients and dosimetric results compared. Another set of 83 plans was realised on the same patients with different planning criteria, by using a simple template with no attempt to manually improve the plan quality. Those plans were employed to train another model, TP-1. The differences between the plans generated by CL-1 and TP-1 for the validation cohort of patients were compared with respect to the differences between the original plans used to build the two models.ResultsThe CL-2 model presented an improvement relative to CL-1, with higher R2 values and better regression plots. The mean doses to parallel organs decreased with CL-2, while D1% to serial organs increased (but not significantly). The different models CL-1 and TP-1 were able to yield plans according to each original strategy.ConclusionA refined RP model allowed the generation of plans with improved quality, mostly for parallel organs at risk and, possibly, also the intrinsic model quality.

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Section: Discussionmentioning

confidence: 99%

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

et al. 2019

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“…The refined model was then used for automatic IMRT planning, by means of which the achievable DVHs were predicted with a 95% confidence interval for each OAR. It is known that the commercial planning system RapidPlan takes the lower bound of the DVH estimate range as the optimization objectives with an attempt to maximize OAR sparing ( 20 ). Based on our experience and the previous study ( 15 ), we selected the predicted mean value instead of the lower limit of the DVH estimation range as the starting optimization objectives for some adjacent OARs such as the optical chiasm, optical nerve, pituitary, and inner ear in advanced T3-T4 cases to better balance the target dose coverage and normal tissue protection.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma

Liu

Xie

et al. 2021

Front. Oncol.

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Background and purposeTo validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.Methods and materialsA knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.ResultsWith comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.ConclusionThe automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.

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“…PQM was first introduced by Nelms 27 ) and prior experience. 28,29 The detailed description can be found in the supplementary materials.…”

Section: C | Plan Quality Assessmentmentioning

confidence: 99%

Limiting treatment plan complexity by applying a novel commercial tool

Scaggion

Fusella

Agnello

et al. 2020

J Applied Clin Med Phys

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Purpose: A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality. Methods: Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination. Results: The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths. Conclusions: This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality. K E Y W O R D S aperture shape controller, complexity metrics, MLC, plan complexity, plan quality metric, treatment planning 1 | INTRODUCTION Volumetric modulated arc therapy (VMAT) is nowadays the standard treatment technique in high-quality radiation therapy delivered with clinical LINACs. The rapid dose fall-off outside target boundaries grants highly conformed dose distributions and near-optimal sparing of surrounding critical structures. Volumetric modulated arc therapy has generally replaced intensity-modulated radiation therapy (IMRT) because of the shorter treatment time obtained through the simultaneous variation of gantry speed, dose rate and MLC position. 1,2 Moreover, VMAT tends to use fewer monitor units (MU) per fraction than IMRT and thus reduces the burden of second malignancies. 3-5

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Efficiently train and validate a RapidPlan model through APQM scoring

Cited by 28 publications

References 15 publications

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma

Limiting treatment plan complexity by applying a novel commercial tool

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