Evaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer

Abstract

Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries. Volumetric modulated arc therapy plans of 60 recent head and neck cancer patients that included sparing of the salivary glands, swallowing muscles, and oral cavity were evenly divided between 2 models, Model(30A) and Model(30B), and were combined in a third model, Model60. Knowledge-based plans were created for 2 evaluation groups: evaluation group 1 (EG1), consisting of 15 recent patients, and evaluation group 2 (EG2), consisting of 15 older patients in whom only the salivary glands were spared. RapidPlan results were compared with clinical plans (CP) for boost and/or elective planning target volume homogeneity index, using HI(B)/HI(E) = 100 × (D2% - D98%)/D50%, and mean dose to composite salivary glands, swallowing muscles, and oral cavity (D(sal), D(swal), and D(oc), respectively). For EG1, RapidPlan improved HI(B) and HI(E) values compared with CP by 1.0% to 1.3% and 1.0% to 0.6%, respectively. Comparable D(sal) and D(swal) values were seen in Model(30A), Model(30B), and Model60, decreasing by an average of 0.1, 1.0, and 0.8 Gy and 4.8, 3.7, and 4.4 Gy, respectively. However, differences were noted between individual organs at risk (OARs), with Model(30B) increasing D(oc) by 0.1, 3.2, and 2.8 Gy compared with CP, Model(30A), and Model60. Plan quality was less consistent when the patient was flagged as an outlier. For EG2, RapidPlan decreased D(sal) by 4.1 to 4.9 Gy on average, whereas HI(B) and HI(E) decreased by 1.1% to 1.5% and 2.3% to 1.9%, respectively. RapidPlan knowledge-based treatment plans were comparable to CP if the patient's OAR-planning target volume geometry was within the range of those included in the models. EG2 results showed that a model including swallowing-muscle and oral-cavity sparing can be applied to patients with only salivary gland sparing. This may allow model library sharing between institutes. Optimal detection of inadequate plans and population of model libraries requires further investigation.