Towards individualized dose constraints: Adjusting the QUANTEC radiation pneumonitis model for clinical risk factors

<div><p></p><p><i>Background.</i> Understanding the dose-response of the lung in order to minimize the risk of radiation pneumonitis (RP) is critical for optimization of lung cancer radiotherapy. We propose a method to combine the dose-response relationship for RP in the landmark QUANTEC paper with known clinical risk factors, in order to enable individual risk prediction. The approach is validated in an independent dataset. <i>Material and methods.</i> The prevalence of risk factors in the patient populations underlying the QUANTEC analysis was estimated, and a previously published method to adjust dose-response relationships for clinical risk factors was employed. Effect size estimates (odds ratios) for risk factors were drawn from a recently published meta-analysis. Baseline values for <i>D</i><sub>50</sub> and γ<sub>50</sub> were found. The method was tested in an independent dataset (103 patients), comparing the predictive power of the dose-only QUANTEC model and the model including risk factors. Subdistribution cumulative incidence functions were compared for patients with high/low-risk predictions from the two models, and concordance indices (c-indices) for the prediction of RP were calculated. <i>Results.</i> The reference dose- response relationship for a patient without pulmonary co-morbidities, caudally located tumor, no history of smoking, < 63 years old, and receiving no sequential chemotherapy was estimated as <i>D</i><sub>50</sub><sup>0</sup> = 34.4 Gy (95% CI 30.7, 38.9), <i>γ</i><sub>50</sub><sup>0</sup> = 1.19 (95% CI 1.00, 1.43). Individual patient risk estimates were calculated. The cumulative incidences of RP in the validation dataset were not significantly different in high/low-risk patients when doing risk allocation with the QUANTEC model (p = 0.11), but were significantly different using the individualized model (p = 0.006). C-indices were significantly different between the dose-only and the individualized model. <i>Conclusion.</i> This study presents a method to combine a published dose-response function with known clinical risk factors and demonstrates the increased predictive power of the combined model. The method allows for individualization of dose constraints and individual patient risk estimates.</p></div>