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Background The recent phase II randomized stereotactic ablative radiotherapy with and without immunotherapy (I-SABR) trial has shown improved event-free survival (EFS) when adding immunotherapy to stereotactic ablative radiotherapy (SABR) for early-stage inoperable non-small cell lung cancer (NSCLC). However, optimizing patient selection thereof is critical, because not every patient benefits from immunotherapy. Leveraging the powerful use of artificial intelligence, this secondary analysis of the I-SABR trial developed a modeling system (named “I-SABR-SELECT”) based on clinical and radiomic factors to address which patients should receive additional immunotherapy.Methods The discovery/validation cohorts were from the I-SABR trial, with external validation from the single-arm STARS trial. Individual treatment effect scores, estimating the benefit of adding immunotherapy, were derived from radiomic and clinical predictors using counterfactual reasoning. Dimensionality reduction was applied to mitigate overfitting and enhance model robustness. We also evaluated the average treatment effect between subgroups of patients who were treated following versus against the model’s recommendation.Results The model recommended that 49% (69/141) patients enrolled in the I-SABR trial switch treatments (65% (49/75) in the SABR arm and 30% (20/66) in the I-SABR arm). Patients treated by the model’s recommendation had higher EFS, with HRs of 0.06 (in the I-SABR arm, p<0.001) and 0.26 (in the SABR alone arm, p=0.0042) from the I-SABR trial population, and 0.38 (p=0.031) for the STARS trial. Following model stratification, among patients recommended for SABR+immunotherapy, the restricted mean survival time for EFS is prolonged by 1.43 years compared to those who received SABR alone. The absolute risk reduction of the added immunotherapy effect was over twofold greater than that observed in the I-SABR trial without selection.Conclusions Combining clinical and radiomic parameters, I-SABR-SELECT uses causal reasoning to individualize treatment selection for patients with early-stage inoperable NSCLC. If validated, it could serve as a foundation for a treatment-focused digital twin by integrating real-time adaptive decision-making. Code: https://github.com/WuLabMDA/ISABR-SELECT