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

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.

Parotid sparing in RapidPlan Oropharynx models: To split or not to split

“…Results from our study indicate no benefit for the contralateral parotid in separating parotid data into ipsilateral and contralateral when constructing models, with a potential improvement in ipsilateral doses. This is similar to results from Fogliata et al who also found little difference in the parotid doses between models. There were also no significant trade‐offs between the results of the OAR's produced by both models which can be seen in Table .…”

“…The first model, known as the ‘separated model’, contains parotid dose data from previously treated patients separated into contralateral and ipsilateral, while the second model, known as the ‘combined model’, combines the parotid data as one structure called ‘parotids’. Fogliata et al also investigated this comparison in a recent study and found that there was little difference between the two models for the resultant parotid dose. This current study sought to further validate this technique.…”

Critical Appraisal of the Treatment Planning Performance of Volumetric Modulated Arc Therapy by Means of a Dual Layer Stacked Multileaf Collimator for Head and Neck, Breast, and Prostate

“… 9 An isotropic 5 mm margin was then added to CTVs to obtain the PTVs which were cropped 4 mm inside the body contour to avoid the derma. These cases were planned as described in the study by Gregoire et al 11 , Franzese et al 12 , and Fogliata et al 13 with a SIB fractionation scheme to deliver 2 dose levels of 69.96 Gy to the primary target volume and 54.45 Gy to the elective volume in 33 fractions. The OARs of first priority considered were the parotids, the spinal cord, the oral cavity, and the larynx.…”

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

“…Well‐trained RapidPlan models have outperformed conventional trial and error‐based manual planning by reducing excess organs‐at‐risk (OAR) dose with greater consistency 17, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30. Should the model performance be highly dependent on the library volume31 and average quality of the training plans,17, 32 incorporating the model‐improved constituent training plans into the model (closed‐loop)25 may potentially evolve the model as a cycle of interactive improvement.…”