A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer

• DOI: 10.1111/crj.70022
• Publication Date: 2024-11-25T05:49:33Z
• Authors: Rongrong Bian; Feng Zhao; Bo Peng; Jin Zhang; Qixing Mao; Lin Wang; Qiang Chen
• Keywords: Nomogram; Gene signature; Concordance

ABSTRACT Background Early‐stage non–small cell lung cancer (NSCLC) is being diagnosed increasingly, and in 30% of patients, recurrence will develop within 5 years. Thus, it urgent to identify recurrence‐related markers optimize the management patient‐tailored therapeutics. Methods The eligible datasets were downloaded from TCGA GEO. In discovery phase, two algorithms, least absolute shrinkage selector operation support vector machine‐recursive feature elimination, used candidate genes. recurrence‐associated signature was developed by penalized Cox regression. nomogram constructed further tested via other independent cohorts. Results this retrospective study, 14 7 published signatures included. A 13‐gene based generated regression categorized training cohort into high‐risk low‐risk subgroups (HR = 8.873, 95% CI: 4.228–18.480 p < 0.001). Furthermore, a integrating signature, age, histology predict recurrence‐free survival cohort, which performed well external validation cohorts (concordance index: 0.737, 0.732–0.742, 0.001; 0.666, 0.650–0.682, 0.651, 0.637–0.665, 0.001, respectively). Jiangsu enrolled 163 patients 2.723, 1.526–4.859, Post‐operative adjuvant therapy achieved evaluated disease‐free high intermediate risk groups 4.791, 1.081–21.231, 0.039). Conclusions proposed promising tool for estimating stage I NSCLC, might have tremendous value early NSCLC guiding strategies.