基于术前 CT 分期 IA 期非小细胞肺癌的预测放射组学模型的建立：淋巴结转移。
Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer.
机构信息
Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei Province, China; Department of Computed Tomography and Magnetic Resonance, Children's Hospital of Hebei Province, 133 Jianhua South Street, Shijiazhuang, 050031, Hebei Province, China.
Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, 12 Jiankang Road, Shijiazhuang, 050011, Hebei Province, China.
出版信息
Lung Cancer. 2020 Jan;139:73-79. doi: 10.1016/j.lungcan.2019.11.003. Epub 2019 Nov 9.
OBJECTIVES
To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients.
METHODS
This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models.
RESULTS
The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both<0.001).
CONCLUSION
A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.
目的
利用临床参数、放射组学特征以及两者的组合，为术前基于 CT 的 IA 期非小细胞肺癌（NSCLC）患者建立并验证预测淋巴结转移（LNM）的预测模型。
方法
本回顾性研究纳入了我院 649 例术前基于 CT 的 IA 期 NSCLC 患者。其中 138 例（21%）患者术后发生 LNM。从静脉期增强 CT 中提取了 396 个放射组学特征。训练组包含 455 例患者（97 例伴 LNM，358 例不伴 LNM），测试组包含 194 例患者（41 例伴 LNM，153 例不伴 LNM）。采用最小绝对收缩和选择算子（LASSO）算法进行放射组学特征选择，采用随机森林（RF）算法建立模型。建立三种模型（临床模型、放射组学模型和联合模型）来预测早期 NSCLC 患者的 LNM。使用三种模型评估 LNM 状态（伴或不伴 LNM）的受试者工作特征（ROC）曲线下面积（AUC）值和决策曲线分析。
结果
ROC 分析（也即决策曲线分析）表明，放射组学模型（训练组和测试组的 AUC 值分别为 0.898 和 0.851）和联合模型（分别为 0.911 和 0.860）对于预测 LNM 具有良好的预测性能。与临床模型（分别为 0.739 和 0.614；delong 检验 p 值均<0.001）相比，两种模型的预测性能均更好。
结论
使用 CE-CT 静脉期的放射组学模型具有预测术前基于 CT 的 IA 期 NSCLC 患者 LNM 的潜力。
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