Development of advanced preselection tools to reduce redundant plan comparisons in model-based selection of head and neck cancer patients for proton therapy. (July 2021) - Tambas, Makbule; van der Laan, Hans P.; Rutgers, Wouter; van den Hoek, Johanna G.M.; Oldehinkel, Edwin; Meijer, Tineke W.H.; van der Schaaf, Arjen; Scandurra, Daniel; Free, Jeffrey; Both, Stefan; Steenbakkers, Roel J.H.M.; Langendijk, Johannes A.
Highlights: A tool is developed to preselect HNC patients for proton therapy. The tool uses data from VMAT plan and/or delineation to predict proton indication. Photon vs. proton plan comparison result was predicted with a 80% accuracy by tool. Redundant plan comparisons were prevented by 68% using the tool. This will significantly increase cost-effectiveness of patient selection for proton. Abstract: Purpose: In the Netherlands, head and neck cancer (HNC) patients are selected for proton therapy (PT) based on estimated normal tissue complication probability differences (ΔNTCP) between photons and protons, which requires a plan comparison (VMAT vs. IMPT). We aimed to develop tools to improve patient selection for plan comparisons. Methods: This prospective study consisted of 141 consecutive patients in which a plan comparison was done. IMPT plans of patients not qualifying for PT were classified as 'redundant'. To prevent redundant IMPT planning, 5 methods that were primarily based on regression models were developed to predict IMPT Dmean to OARs, by using data from VMAT plans and volumetric data from delineated targets and OARs. Then, actual and predicted plan comparison outcomes were compared. The endpoint was being selected for proton therapy. Results: Seventy out of 141 patients (49.6%) qualified for PT. Using the developed preselection tools, redundant IMPT planning could have been prevented in 49–68% of the remaining 71 patients not qualifying for PT (=specificity) when the sensitivity of all methods was fixed to 100%, i.e., no false negative cases (positive predictive value range: 57–68%, negative predictive value: 100%). Conclusion: The advanced preselection tools, which uses volume and VMAT dose data, prevented labour intensive creation of IMPT plans in up to 68% of non-qualifying patients for PT. No patients qualifying for PT would have been incorrectly denied a plan comparison. This method contributes significantly to a more cost-effective model-based selection of HNC patients for PT.