<p>Thoracic radiotherapy (TRT) has been proposed as a potentially beneficial adjunct to first-line targeted therapy or chemoimmunotherapy in metastatic non-small cell lung cancer (NSCLC). This commentary discusses the retrospective study by Xue et al. and highlights several methodological issues that may influence interpretation of the reported survival benefit. Although the observed improvement in overall survival is clinically encouraging, imbalances in metastatic burden, inconsistent incorporation of key prognostic variables, and limited maturity of survival data may introduce residual confounding. Additional concerns include the causal interpretation of multivariable models, overlapping definitions of pulmonary toxicity, time-dependent adverse events, and unadjusted subgroup analyses. Overall, TRT may benefit carefully selected patients, but prospective studies and robust real-world analyses using causal-inference methods are needed to define optimal candidates and ensure safe implementation.</p>

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Interpreting the apparent benefit of thoracic radiotherapy in metastatic NSCLC: patient selection and analytical issues remain important—a comment on Xue et al

  • Lili Qin,
  • Shasha Wang,
  • Haifeng Qin

摘要

Thoracic radiotherapy (TRT) has been proposed as a potentially beneficial adjunct to first-line targeted therapy or chemoimmunotherapy in metastatic non-small cell lung cancer (NSCLC). This commentary discusses the retrospective study by Xue et al. and highlights several methodological issues that may influence interpretation of the reported survival benefit. Although the observed improvement in overall survival is clinically encouraging, imbalances in metastatic burden, inconsistent incorporation of key prognostic variables, and limited maturity of survival data may introduce residual confounding. Additional concerns include the causal interpretation of multivariable models, overlapping definitions of pulmonary toxicity, time-dependent adverse events, and unadjusted subgroup analyses. Overall, TRT may benefit carefully selected patients, but prospective studies and robust real-world analyses using causal-inference methods are needed to define optimal candidates and ensure safe implementation.