Predicting regional recurrence and prognosis in stereotactic body radiotherapy-treated clinical stage I non-small cell lung cancer using a radiomics model constructed with surgical data.

BACKGROUND:Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT). PURPOSE:To develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients. METHODS:Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A pre-operative CT-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the three models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from two academic medical centers. RESULTS:A total of 769 patients were included. Eight CT features were identified in the radiomics model, achieving areas under the curves (AUCs) of 0.85 (95% CI 0.81-0.89) and 0.83 (95% CI 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (p<0.001) and shorter regional recurrence-free survival (p=0.02), progression-free survival (p=0.004) and overall survival (p=0.006) than those in the low-risk group. CONCLUSION:The radiomics model based on pathologically confirmed data effectively identified patients with ONLM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.