Causal AI-Based Clinical and Radiomic Analysis for Optimizing Patient Selection in Combined Immunotherapy and Sabr in Early-Stage NSCLC: A Secondary Analysis of the Phase II I-SABR Trial

Authors

Maliazurina B Saad
Eman Showkatian
Vivek Verma
Qasem Al-Tashi
Muhammad Aminu
Xinyan Xu
Muhamed Qayati Mohamed
Morteza Salehjahromi
Sheeba J Sujit
Yuliya Kitsel
Steven H Lin
Zhongxing Liao
Saumil Gandhi
David Qian
David Jaffray
Caroline Chung
Natalie I Vokes
Jianjun Zhang
J Jack Lee
John V Heymach
Jia Wu
Joe Y Chang

Language

English

Publication Date

10-5-2025

Journal

The Journal for ImmunoTherapy of Cancer

DOI

10.1136/jitc-2025-013074

PMID

41052882

PMCID

PMC12506444

PubMedCentral® Posted Date

10-5-2025

PubMedCentral® Full Text Version

Post-print

Abstract

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.

Keywords

Humans, Carcinoma, Non-Small-Cell Lung, Lung Neoplasms, Immunotherapy, Radiosurgery, Patient Selection, Artificial Intelligence, Male, Female, Neoplasm Staging, Middle Aged, Aged, Combined Modality Therapy, Radiomics, Biomarker, Lung Cancer, Radiotherapy/radioimmunotherapy, Survivorship, Statistics

Comments

Code: https://github.com/WuLabMDA/ISABR-SELECT

Published Open-Access

yes

Recommended Citation

Saad, Maliazurina B; Showkatian, Eman; Verma, Vivek; et al., "Causal AI-Based Clinical and Radiomic Analysis for Optimizing Patient Selection in Combined Immunotherapy and Sabr in Early-Stage NSCLC: A Secondary Analysis of the Phase II I-SABR Trial" (2025). Faculty, Staff and Student Publications. 5822.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5822