Abstract

To generate a prediction model for selection of treatment modality for early-stage non-small cell lung cancer (NSCLC). Stereotactic body radiotherapy (SBRT) and minimally invasive surgery (MIS) are used in the local treatment of early-stage NSCLC. However, selection of patients for either SBRT or MIS remains challenging, due to the multitude of factors influencing the decision-making process. We analyzed 1291 patients with clinical stage I NSCLC treated with intended MIS or SBRT from January 2020 to July 2023. A prediction model for selection for SBRT was created based on multivariable logistic regression analysis. The receiver operating characteristic curve analysis stratified the cohort into 3 treatment-related risk categories. Post-procedural outcomes, recurrence and overall survival (OS) were investigated to assess the performance of the model. In total, 1116 patients underwent MIS and 175 SBRT. The prediction model included age, performance status, previous pulmonary resection, MSK-Frailty score, FEV1 and DLCO, and demonstrated an area-under-the-curve of 0.908 (95%CI, 0.876-0.938). Based on the probability scores (n=1197), patients were stratified into a low-risk (MIS, n=970 and SBRT, n=28), intermediate-risk (MIS, n=96 and SBRT, n=53) and high-risk category (MIS, n=10 and SBRT, n=40). Treatment modality was not associated with OS (HR of SBRT, 1.67 [95%CI: 0.80-3.48]; P=0.20). Clinical expertise can be translated into a robust predictive model, guiding the selection of stage I NSCLC patients for MIS versus SBRT and effectively categorizing them into three distinct risk groups. Patients in the intermediate category could benefit most from multidisciplinary evaluation.

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