Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
Bayesian personalized dose constraints in selecting patients with non-small cell lung cancer for cardiac risk-adaptive treatment.
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Chen Mei, Xu Tianlin, Xu Ting, Maguire Rachel C, Chen Xinru, Koutroumpakis Efstratios, Deswal Anita, Ajdari Ali, Niedzielski Joshua S, Yang Jinzhong, Meng Qing H, Mohan Radhe, Lin Ruitao, Zhang Xiaodong, Liao Zhongxing
Аннотация
PURPOSE: To develop a personalized approach for selecting patients with non-small cell lung cancer (NSCLC) for cardiac risk-adaptive treatment by creating a normal-tissue complication probability (NTCP) model that accounts for heterogeneous radiation dose effects and deriving personalized dose constraints that incorporate model uncertainty.
METHODS AND MATERIALS: We analyzed a training cohort of 160 patients from a completed prospective trial and a validation cohort of 91 patients from an ongoing trial. The endpoint was high-sensitivity cardiac troponin T (hs-cTnT) elevation > 5 ng/L during radiotherapy. A Bayesian hierarchical NTCP model based on risk stratification by decision tree was developed to predict the risk of hs-cTnT elevation, treating mean heart dose (MHD) as a group-specific effect. To address model uncertainty in deriving personalized dose constraints, the probability cut-off parameter was optimized to maximize sensitivity and specificity based on posterior distributions. The patient selection accuracy of the uncertainty-incorporated dose constraints was compared against the conventional point-estimate-based ones in internal validation, same-institution external validation, and prospective implementation testing.
RESULTS: Patients were stratified into 3 risk subgroups based on tumor location and age. The MHD strongly affected patients aged > 64 years with left/mediastinal tumors (odds ratio = 2.16 [95 % credible interval = 1.07-4.14]). The uncertainty-incorporated dose constraints outperformed point-estimate-based dose constraints in specificity (0.57-0.68 vs 0.28-0.51) and accuracy (0.60-0.69 vs 0.43-0.59) across validations.
CONCLUSION: Based on our Bayesian hierarchical NTCP model, we proposed using uncertainty-incorporated personalized MHD constraints to select patients with NSCLC for cardiac risk-adaptive treatment. This framework represents an important step toward personalized radiotherapy to reduce cardiac toxicity.
METHODS AND MATERIALS: We analyzed a training cohort of 160 patients from a completed prospective trial and a validation cohort of 91 patients from an ongoing trial. The endpoint was high-sensitivity cardiac troponin T (hs-cTnT) elevation > 5 ng/L during radiotherapy. A Bayesian hierarchical NTCP model based on risk stratification by decision tree was developed to predict the risk of hs-cTnT elevation, treating mean heart dose (MHD) as a group-specific effect. To address model uncertainty in deriving personalized dose constraints, the probability cut-off parameter was optimized to maximize sensitivity and specificity based on posterior distributions. The patient selection accuracy of the uncertainty-incorporated dose constraints was compared against the conventional point-estimate-based ones in internal validation, same-institution external validation, and prospective implementation testing.
RESULTS: Patients were stratified into 3 risk subgroups based on tumor location and age. The MHD strongly affected patients aged > 64 years with left/mediastinal tumors (odds ratio = 2.16 [95 % credible interval = 1.07-4.14]). The uncertainty-incorporated dose constraints outperformed point-estimate-based dose constraints in specificity (0.57-0.68 vs 0.28-0.51) and accuracy (0.60-0.69 vs 0.43-0.59) across validations.
CONCLUSION: Based on our Bayesian hierarchical NTCP model, we proposed using uncertainty-incorporated personalized MHD constraints to select patients with NSCLC for cardiac risk-adaptive treatment. This framework represents an important step toward personalized radiotherapy to reduce cardiac toxicity.