列线图
无线电技术
医学
新辅助治疗
肺癌
逻辑回归
放射科
肿瘤科
阶段(地层学)
内科学
癌症
生物
古生物学
乳腺癌
作者
Chaoyuan Liu,Wei Zhao,Junpeng Xie,Huashan Lin,Xingsheng Hu,Chang Li,Youlan Shang,Yapeng Wang,Yingjia Jiang,Meng-Ge Ding,Muyun Peng,Tian Xu,Ao’ran Hu,Yuda Huang,Yuan Gao,Xianling Liu,Jun Li,Fang Ma
标识
DOI:10.3389/fimmu.2023.1115291
摘要
The treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects. We are currently unable to accurately predict therapeutic response. We aimed to develop a radiomics-based nomogram to predict a major pathological response (MPR) of potentially resectable NSCLC to neoadjuvant immunochemotherapy using pretreatment computed tomography (CT) images and clinical characteristics.A total of 89 eligible participants were included and randomly divided into training (N=64) and validation (N=25) sets. Radiomic features were extracted from tumor volumes of interest in pretreatment CT images. Following data dimension reduction, feature selection, and radiomic signature building, a radiomics-clinical combined nomogram was developed using logistic regression analysis.The radiomics-clinical combined model achieved excellent discriminative performance, with AUCs of 0.84 (95% CI, 0.74-0.93) and 0.81(95% CI, 0.63-0.98) and accuracies of 80% and 80% in the training and validation sets, respectively. Decision curves analysis (DCA) indicated that the radiomics-clinical combined nomogram was clinically valuable.The constructed nomogram was able to predict MPR to neoadjuvant immunochemotherapy with a high degree of accuracy and robustness, suggesting that it is a convenient tool for assisting with the individualized management of patients with potentially resectable NSCLC.
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