Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer

Purpose: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' ' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetric parameters. However, dosimetric parameters, e.g. dose-volume (DV) indices, are highly correlated with one another and are only weakly associated with RIL. Here we introduce the novel concept of " composite dosimetric score" " (CDS) as the index that condenses the dose distribution in immune tissues of interest to study the dosimetric dependence of RIL. We derived an improved multivariate classification fi cation scheme for risk of grade 4 RIL (G4RIL), based on this novel RT dosimetric feature, for patients receiving chemo RT for esophageal cancer.