Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

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

Late toxicity in the randomized multicenter HYPRO trial for prostate cancer analyzed with automated treatment planning

Purpose/objective: Assess to what extent the use of automated treatment planning would have reduced organ-at-risk dose delivery observed in the randomized HYPRO trial for prostate cancer, and estimate related toxicity reductions. Investigate to what extent improved plan quality for hypofractionation scheme as achieved with automated planning can potentially reduce observed enhanced toxicity for the investigated hypofractionation scheme to levels observed for conventional fractionation scheme. Material/methods: For 725 trial patients, VMAT plans were generated with an algorithm for automated multi-criterial plan generation (autoVMAT). All clinically delivered plans (CLINICAL), generated with commonly applied interactive trial-and-error planning were also available for the investigations. Analyses were based on dose-volume histograms (DVH) and predicted normal tissue complication probabilities (NTCP) for late gastrointestinal (GI) toxicity. Results: Compared to CLINICAL, autoVMAT plans had similar or higher PTV coverage, while large and statistically significant OAR sparing was achieved. Mean doses in the rectum, anus and bladder were reduced by 7.8 +/- 4.7 Gy, 7.9 +/- 6.0 Gy and 4.2 +/- 2.9 Gy, respectively (p <0.001). NTCPs for late grade >= 2 GI toxicity, rectal bleeding and stool incontinence were reduced from 23.3 +/- 9.1% to 19.7 +/- 8.9%, from 9.7 +/- 2.8% to 8.2 +/- 2.8%, and from 16.8 +/- 8.5% to 13.1 +/- 7.2%, respectively (p <0.001). Reductions in rectal bleeding NTCP were observed for all published Equivalent Uniform Dose volume parameters, n. AutoVMAT allowed hypofractionation with predicted toxicity similar to conventional fractionation with CLINICAL plans. Conclusion: Compared to CLINICAL, autoVMAT had superior plan quality, with meaningful NTCP reductions for both conventional fractionation and hypofractionation schemes. AutoVMAT plans might reduce toxicity for hypofractionation to levels that were clinically observed (and accepted) for conventional fractionation. This may be relevant when considering clinical use of the investigated hypofractionation schedule with relatively high fraction dose (3.4 Gy). (C) 2018 Elsevier B.V. All rights reserved

The impact of treatment accuracy on proton therapy patient selection for oropharyngeal cancer patients

textabstractBackground and purpose The impact of treatment accuracy on NTCP-based patient selection for proton therapy is currently unknown. This study investigates this impact for oropharyngeal cancer patients. Materials and methods Data of 78 patients was used to automatically generate treatment plans for a simultaneously integrated boost prescribing 70 GyRBE/54.25 GyRBE in 35 fractions. IMRT treatment plans were generated with three different margins; intensity modulated proton therapy (IMPT) plans for five different setup and range robustness settings. Four NTCP models were evaluated. Patients were selected for proton therapy if NTCP reduction was ≥10% or ≥5% for grade II or III complications, respectively. Results The degree of robustness had little impact on patient selection for tube feeding dependence, while the margin had. For other complications the impact of the robustness setting was noticeably higher. For high-precision IMRT (3 mm margin) and high-precision IMPT (3 mm setup/3% range error), most patients were selected for proton therapy based on problems swallowing solid food (51.3%) followed by tube feeding dependence (37.2%), decreased parotid flow (29.5%), and patient-rated xerostomia (7.7%). Conclusions Treatment accuracy has a significant impact on the number of patients selected for proton therapy. Therefore, it cannot be ignored in estimating the number of patients for proton therapy

Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

<p><b>Background:</b> 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.</p> <p><b>Material and Methods:</b> 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.</p> <p><b>Results:</b> 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%.</p> <p><b>Conclusion:</b> 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.</p