基于影像组学的Nomogram 模型预测局部晚期非小细胞肺癌根治性放化疗后局部区域失败风险
Radiomics Nomogram for Predicting Locoregional Failure in Locally Advanced Non-small Cell Lung Cancer Treated with Definitive Chemoradiotherapy.
机构信息
Department of Graduate, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China; Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117, China.
Department of Graduate, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
出版信息
Acad Radiol. 2022 Feb;29 Suppl 2:S53-S61. doi: 10.1016/j.acra.2020.11.018. Epub 2020 Dec 8.
RATIONALE AND OBJECTIVES
To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting locoregional failure (LRF) in patients with locally advanced non-small cell lung cancer (NSCLC) treated with definitive chemoradiotherapy (CRT).
MATERIALS AND METHODS
A total of 141 patients with locally advanced NSCLC treated with definitive CRT from January 2014 to December 2017 were included and divided into testing cohort (n = 100) and validation (n = 41) cohort. Radiomics features were extracted from pretreatment contrast enhanced CT. The least absolute shrinkage and selection operator logistic regression was processed to select predictive features from the testing cohort and constructed a radiomics signature. Clinical characteristics and the radiomics signature were analyzed using univariable and multivariate Cox regression. The radiomics nomogram was established with the radiomics signature and independent clinical factors. Harrell's C-index, calibration curves and decision curves were used to assess the performance of the radiomics nomogram.
RESULTS
The radiomics signature, which consisted of eight selected features, was an independent factor of LRF. The clinical predictors of LRF were the histologic type and clinical stage. The radiomics nomogram combined with the radiomics signature and clinical prognostic factors showed good performance with C-indexes of 0.796 (95% confidence interval [CI]: 0.709-0.883) and 0.756 (95% CI: 0.674-0.838) in the testing and validation cohorts respectively. Additionally, the combined nomogram resulted in better performance (p < 0.001) for the estimation of LRF than the nomograms with the radiomics signature (C-index: 0.776; 95% CI: 0.686-0.866) or clinical predictors (C-index: 0.641; 95% CI: 0.542-0.740) alone.
CONCLUSION
The radiomics nomogram provided the best performance for LRF prediction in patients with locally advanced NSCLC, which may help optimize individual treatments.
背景与目的
开发并验证一种基于计算机断层扫描（CT）的放射组学列线图，用于预测接受根治性放化疗（CRT）的局部晚期非小细胞肺癌（NSCLC）患者的局部区域失败（LRF）。
材料与方法
共纳入 141 例 2014 年 1 月至 2017 年 12 月接受根治性 CRT 的局部晚期 NSCLC 患者，分为检测队列（n=100）和验证队列（n=41）。从预处理增强 CT 中提取放射组学特征。通过最小绝对收缩和选择算子逻辑回归从检测队列中选择预测特征并构建放射组学特征。使用单变量和多变量 Cox 回归分析临床特征和放射组学特征。使用放射组学特征和独立临床因素建立放射组学列线图。Harrell's C-index、校准曲线和决策曲线用于评估放射组学列线图的性能。
结果
由 8 个选定特征组成的放射组学特征是 LRF 的独立因素。LRF 的临床预测因素是组织学类型和临床分期。放射组学列线图结合放射组学特征和临床预后因素，在检测队列和验证队列中的 C 指数分别为 0.796（95%置信区间 [CI]：0.709-0.883）和 0.756（95%CI：0.674-0.838），具有良好的性能。此外，联合列线图在估计 LRF 方面的性能优于仅基于放射组学特征（C 指数：0.776；95%CI：0.686-0.866）或临床预测因素（C 指数：0.641；95%CI：0.542-0.740）的列线图（p<0.001）。
结论
放射组学列线图在预测局部晚期 NSCLC 患者的 LRF 方面表现最佳，可能有助于优化个体化治疗。
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