Yang Minglei, Ren Yijiu, She Yunlang, Xie Dong, Sun Xiwen, Shi Jingyun, Zhao Guofang, Chen Chang
Department of Cardiothoracic Surgery, Ningbo No. 2 Hospital, Ningbo 315012, China.
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200443, China.
Ann Transl Med. 2019 Jun;7(12):259. doi: 10.21037/atm.2019.05.20.
Dry pleural dissemination (DPD) in non-small cell lung cancer (NSCLC) is defined as having solid pleural metastases without malignant pleural effusion. We aim to identify DPD by applying radiomics, a novel approach to decode the tumor phenotype.
Preoperative chest computed tomographic images and basic clinical feature were retrospectively evaluated in patients with surgically resected NSCLC between January 1, 2015 and December 31, 2016. Propensity score was applied to match the DPD and non-DPD groups. One thousand and eighty radiomics features were quantitatively extracted by the software and "pyradiomics" package. Least absolute shrinkage and selection operator (LASSO) binary model was applied for feature selection and developing the radiomics signature. The discrimination was evaluated using area under the curve (AUC) and Youden index.
Sixty-four DPD patients and paired 192 non-DPD patients were enrolled. Using the LASSO model, this study developed a radiomics signature including 10 radiomic features. The mean ± standard deviation values of the radiomics signature with DPD status (-2.129±1.444) was significantly higher compared to those with non-DPD disease (0.071±0.829, P<0.001). The ten-feature based signature showed good discrimination between DPD and non-DPD, with an AUC of 0.93 (95% confidence-interval, 0.891-0.958) respectively. The sensitivity and specificity of the radiomics signature was 85.94% and 85.94%, with the optimal cut-off value of -0.696 and Youden index of 0.71.
The signature based on radiomics features can provide potential predictive value to identify DPD in patients with NSCLC.
非小细胞肺癌（NSCLC）中的干性胸膜播散（DPD）定义为存在实性胸膜转移但无恶性胸腔积液。我们旨在通过应用放射组学这一解码肿瘤表型的新方法来识别DPD。
回顾性评估2015年1月1日至2016年12月31日期间接受手术切除的NSCLC患者的术前胸部计算机断层扫描图像和基本临床特征。应用倾向评分匹配DPD组和非DPD组。通过软件和“pyradiomics”包定量提取1080个放射组学特征。应用最小绝对收缩和选择算子（LASSO）二元模型进行特征选择并建立放射组学特征。使用曲线下面积（AUC）和尤登指数评估鉴别能力。
纳入64例DPD患者和配对的192例非DPD患者。本研究使用LASSO模型建立了一个包含10个放射组学特征的放射组学特征。DPD状态的放射组学特征的平均值±标准差（-2.129±1.444）显著高于非DPD疾病患者（0.071±0.829，P<0.001）。基于这10个特征的特征在DPD和非DPD之间显示出良好的鉴别能力，AUC分别为0.93（95%置信区间，0.891-0.958）。放射组学特征的敏感性和特异性分别为85.94%和85.94%，最佳截断值为-0.696，尤登指数为0.71。
基于放射组学特征的特征可为识别NSCLC患者中的DPD提供潜在的预测价值。
