BACKGROUND: Proton therapy is becoming increasingly available, so it is important to apply objective and individualized patient selection to identify those who are expected to benefit most from proton therapy compared to conventional intensity modulated radiation therapy (IMRT). Comparative treatment planning using normal tissue complication probability (NTCP) evaluation has recently been proposed. This work investigates the impact of NTCP model and dose uncertainties on model-based patient selection. MATERIAL AND METHODS: We used IMRT and intensity modulated proton therapy (IMPT) treatment plans of 78 oropharyngeal cancer patients, which were generated based on automated treatment planning and evaluated based on three published NTCP models. A reduction in NTCP of more than a certain threshold (e.g. 10% lower NTCP) leads to patient selection for IMPT, referred to as 'nominal' selection. To simulate the effect of uncertainties in NTCP-model coefficients (based on reported confidence intervals) and planned doses on the accuracy of model-based patient selection, the Monte Carlo method was used to sample NTCP-model coefficients and doses from a probability distribution centered at their nominal values. Patient selection accuracy within a certain sample was defined as the fraction of patients which had similar selection in both the 'nominal' and 'sampled' scenario. RESULTS: For all three NTCP models, the median patient selection accuracy was found to be above 70% when only NTCP-model uncertainty was considered. Selection accuracy decreased with increasing uncertainty resulting from differences between planned and delivered dose. In case of excessive dose uncertainty, selection accuracy decreased to 60%. CONCLUSION: Model and dose uncertainty highly influence the accuracy of model-based patient selection for proton therapy. A reduction of NTCP-model uncertainty is necessary to reach more accurate model-based patient selection.
BACKGROUND: Proton therapy is becoming increasingly available, so it is important to apply objective and individualized patient selection to identify those who are expected to benefit most from proton therapy compared to conventional intensity modulated radiation therapy (IMRT). Comparative treatment planning using normal tissue complication probability (NTCP) evaluation has recently been proposed. This work investigates the impact of NTCP model and dose uncertainties on model-based patient selection. MATERIAL AND METHODS: We used IMRT and intensity modulated proton therapy (IMPT) treatment plans of 78 oropharyngeal cancerpatients, which were generated based on automated treatment planning and evaluated based on three published NTCP models. A reduction in NTCP of more than a certain threshold (e.g. 10% lower NTCP) leads to patient selection for IMPT, referred to as 'nominal' selection. To simulate the effect of uncertainties in NTCP-model coefficients (based on reported confidence intervals) and planned doses on the accuracy of model-based patient selection, the Monte Carlo method was used to sample NTCP-model coefficients and doses from a probability distribution centered at their nominal values. Patient selection accuracy within a certain sample was defined as the fraction of patients which had similar selection in both the 'nominal' and 'sampled' scenario. RESULTS: For all three NTCP models, the median patient selection accuracy was found to be above 70% when only NTCP-model uncertainty was considered. Selection accuracy decreased with increasing uncertainty resulting from differences between planned and delivered dose. In case of excessive dose uncertainty, selection accuracy decreased to 60%. CONCLUSION: Model and dose uncertainty highly influence the accuracy of model-based patient selection for proton therapy. A reduction of NTCP-model uncertainty is necessary to reach more accurate model-based patient selection.
Authors: Alexander R Delaney; Lei Dong; Anthony Mascia; Wei Zou; Yongbin Zhang; Lingshu Yin; Sara Rosas; Jan Hrbacek; Antony J Lomax; Ben J Slotman; Max Dahele; Wilko F A R Verbakel Journal: Cancers (Basel) Date: 2018-11-02 Impact factor: 6.639
Authors: Harald Paganetti; Chris Beltran; Stefan Both; Lei Dong; Jacob Flanz; Keith Furutani; Clemens Grassberger; David R Grosshans; Antje-Christin Knopf; Johannes A Langendijk; Hakan Nystrom; Katia Parodi; Bas W Raaymakers; Christian Richter; Gabriel O Sawakuchi; Marco Schippers; Simona F Shaitelman; B K Kevin Teo; Jan Unkelbach; Patrick Wohlfahrt; Tony Lomax Journal: Phys Med Biol Date: 2021-02-26 Impact factor: 4.174