BACKGROUND: 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. MATERIAL AND METHODS: 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 D50 and γ50 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. RESULTS: 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 D50(0) = 34.4 Gy (95% CI 30.7, 38.9), γ50(0) = 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. CONCLUSION: 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.
BACKGROUND: 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. MATERIAL AND METHODS: 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 D50 and γ50 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. RESULTS: 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 D50(0) = 34.4 Gy (95% CI 30.7, 38.9), γ50(0) = 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. CONCLUSION: 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.
Authors: M Thor; Jo Deasy; A Iyer; E Bendau; A Fontanella; A Apte; E Yorke; A Rimner; A Jackson Journal: Radiother Oncol Date: 2019-05-27 Impact factor: 6.280
Authors: Sarah L Kerns; Suman Kundu; Jung Hun Oh; Sandeep K Singhal; Michelle Janelsins; Lois B Travis; Joseph O Deasy; A Cecile J E Janssens; Harry Ostrer; Matthew Parliament; Nawaid Usmani; Barry S Rosenstein Journal: Semin Radiat Oncol Date: 2015-05-15 Impact factor: 5.934
Authors: Isabel L Jackson; Fitsum Baye; Chirayu P Goswami; Barry P Katz; Andrew Zodda; Radmila Pavlovic; Ganga Gurung; Don Winans; Zeljko Vujaskovic Journal: Dis Model Mech Date: 2017-01-26 Impact factor: 5.758
Authors: Narek Shaverdian; Maria Thor; Annemarie F Shepherd; Michael D Offin; Andrew Jackson; Abraham J Wu; Daphna Y Gelblum; Ellen D Yorke; Charles B Simone; Jamie E Chaft; Matthew D Hellmann; Daniel R Gomez; Andreas Rimner; Joseph O Deasy Journal: Cancer Med Date: 2020-05-06 Impact factor: 4.452