RapidPlan head and neck model: the objectives and possible clinical benefit

A Fogliata,C Franzese,P Mancosu,G Reggiori,A Stravato,F Lobefalo,L Cozzi,D Franceschini,S Tomatis,M Scorsetti

doi:10.1186/s13014-017-0808-x

A Fogliata, C Franzese + Show 8 more

Open Access

https://doi.org/10.1186/s13014-017-0808-x

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Abstract

BackgroundTo evaluate a knowledge based planning model for RapidPlan (RP) generated for advanced head and neck cancer (HNC) patient treatments, as well its ability to possibly improve the clinical plan quality. The stability of the model was assessed also for a different beam geometry, different dose fractionation and different management of bilateral structures (parotids).MethodsDosimetric and geometric data from plans of 83 patients presenting HNC were selected for the model training. All the plans used volumetric modulated arc therapy (VMAT, RapidArc) to treat two targets at dose levels of 69.96 and 54.45 Gy in 33 fractions with simultaneous integrated boost. Two models were generated, the first separating the ipsi- and contra-lateral parotids, while the second associating the two parotids to a single structure for training. The optimization objectives were adjusted to the final model to better translate the institutional planning and dosimetric strategies and trade-offs. The models were validated on 20 HNC patients, comparing the RP generated plans and the clinical plans. RP generated plans were also compared between the clinical beam arrangement and a simpler geometry, as well as for a different fractionation scheme.ResultsRP improved significantly the clinical plan quality, with a reduction of 2 Gy, 5 Gy, and 10 Gy of the mean parotid, oral cavity and laryngeal doses, respectively. A simpler beam geometry was deteriorating the plan quality, but in a small amount, keeping a significant improvement relative to the clinical plan. The two models, with one or two parotid structures, showed very similar results. NTCP evaluations indicated the possibility of improving (NTCP decreasing of about 7%) the toxicity profile when using the RP solution.ConclusionsThe HNC RP model showed improved plan quality and planning stability for beam geometry and fractionation. An adequate choice of the objectives in the model is necessary for the trade-offs strategies.

Highlights

To evaluate a knowledge based planning model for RapidPlan (RP) generated for advanced head and neck cancer (HNC) patient treatments, as well its ability to possibly improve the clinical plan quality
For a new patient, estimated dose volume histograms (DVH) for the organs at risk (OAR) are generated by the application of the trained model and translated into optimization objectives used by the inverse planning optimisation engines
The potential outliers highlighted in the model log file, or pointed out in the Model Analytics (MA) tool were evaluated case by case

Summary

Introduction

To evaluate a knowledge based planning model for RapidPlan (RP) generated for advanced head and neck cancer (HNC) patient treatments, as well its ability to possibly improve the clinical plan quality. With KBP, a number of optimal plans (judged to be the optimum) generated following the clinical criteria and appropriate trade-off requirements, are used to build and train a mathematical model capable to estimate OAR dose volume histograms (DVH) for any new patient. The RapidPlan (RP) process was appraised in different anatomical sites: liver [10], head and neck [11], nasopharyngeal cancer patients [12], lung and prostate [13], breast [14], pelvis as prostate and cervical cancers [15], oesophagus [16], lung SBRT [17] Those studies proved the possibility to build, train and apply a variety of models leading to improved quality of the treatment plans and better homogeneity of results. The RP model configuration process in all the needed steps is still to be fully understood

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