Yang Minglei, She Yunlang, Deng Jiajun, Wang Tingting, Ren Yijiu, Su Hang, Wu Junqi, Sun Xiwen, Jiang Gening, Fei Ke, Zhang Lei, Xie Dong, Chen Chang
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China.
Department of Thoracic Surgery, Ningbo No.2 Hospital, Chinese Academy of Sciences, Ningbo 315010, China.
Transl Lung Cancer Res. 2019 Dec;8(6):876-885. doi: 10.21037/tlcr.2019.11.18.
Risk stratification of N2 disease is vital for selecting candidates to receive invasive mediastinal staging modalities. In this study, we aimed to stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma using radiomics analysis.
Two datasets of patients with clinical stage I lung adenocarcinoma who underwent lung resection were included (training dataset, 880; validation dataset, 322). Using , 1,078 computed tomography (CT)-based radiomics features were extracted after semi-automated lung nodule segmentation. In order to predict N2 status, a radiomics signature was constructed after selecting the optimal radiomics feature subset by sequentially applying minimum-redundancy-maximum-relevance and least absolute shrinkage and selection operator (LASSO) techniques. Its performance was validated in the validation dataset.
The incidences of N2 metastasis were 8.4% and 7.1% in the training and validation datasets, respectively. Unsupervised cluster analysis revealed that radiomics features significantly correlated with lymph node status and pathological subtypes. For N2 disease prediction, five radiomics features were selected to establish the radiomics signature, which showed a significantly better predictive performance than clinical factors (P<0.001). The area under the receiver operating characteristic curve was 0.81 (0.77-0.86) and 0.69 (0.63-0.75) for radiomics signature and clinical factors, respectively, in the training dataset, and 0.82 (0.71-0.92) and 0.64 (0.52-0.75), respectively, in the validation dataset.
The established CT-based radiomics signature could stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma, thus assisting clinicians in making patient-specific mediastinal staging strategy.
N2期疾病的风险分层对于选择接受侵入性纵隔分期检查的患者至关重要。在本研究中，我们旨在通过放射组学分析对临床I期肺腺癌患者的N2转移风险进行分层。
纳入两个接受肺切除术的临床I期肺腺癌患者数据集（训练数据集，880例；验证数据集，322例）。通过半自动肺结节分割后，提取了1078个基于计算机断层扫描（CT）的放射组学特征。为了预测N2状态，通过依次应用最小冗余最大相关和最小绝对收缩与选择算子（LASSO）技术选择最佳放射组学特征子集后，构建了放射组学特征。其性能在验证数据集中得到验证。
训练数据集和验证数据集中N2转移的发生率分别为8.4%和7.1%。无监督聚类分析显示，放射组学特征与淋巴结状态和病理亚型显著相关。对于N2疾病预测，选择了五个放射组学特征来建立放射组学特征，其预测性能明显优于临床因素（P<0.001）。在训练数据集中，放射组学特征和临床因素的受试者操作特征曲线下面积分别为0.81（0.77 - 0.86）和0.69（0.63 - 0.75），在验证数据集中分别为0.82（0.71 - 0.92）和0.64（0.52 - 0.75）。
所建立的基于CT的放射组学特征可以对临床I期肺腺癌患者的N2转移风险进行分层，从而帮助临床医生制定针对患者的纵隔分期策略。
