Chen Donglai, She Yunlang, Wang Tingting, Xie Huikang, Li Jian, Jiang Gening, Chen Yongbing, Zhang Lei, Xie Dong, Chen Chang
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Eur J Cardiothorac Surg. 2020 Jul 1;58(1):51-58. doi: 10.1093/ejcts/ezaa011.
As evidence has proven that sublobar resection is oncologically contraindicated by tumour spread through air spaces (STAS), its preoperative recognition is vital in customizing surgical strategies. We aimed to assess the value of radiomics in predicting STAS in stage I lung adenocarcinoma.
We retrospectively reviewed the patients with stage I lung adenocarcinoma, who accepted curative resection in our institution between January 2011 and December 2013. Using 'PyRadiomics' package, 88 radiomics features were extracted from computed tomography (CT) images and a prediction model was consequently constructed using Naïve Bayes machine-learning approach. The accuracy of the model was assessed through receiver operating curve analysis, and the performance of the model was validated both internally and externally.
A total of 233 patients were included as the training cohort with 69 (29.6%) patients being STAS (+). Patients with STAS had worse recurrence-free survival and overall survival (P < 0.001). After feature extraction, 5 most contributing radiomics features were selected out to develop a Naïve Bayes model. In the internal validation, the model exhibited good performance with an area under the curve value of 0.63 (0.55-0.71). External validation was conducted on a test cohort with 112 patients and produced an area under the curve value of 0.69.
CT-based radiomics is valuable in preoperatively predicting STAS in stage I lung adenocarcinoma, which may aid surgeons in determining the optimal surgical approach.
由于有证据表明，通过气腔扩散的肿瘤（STAS）在肿瘤学上是肺叶下切除的禁忌证，因此术前识别对于制定手术策略至关重要。我们旨在评估影像组学在预测I期肺腺癌STAS中的价值。
我们回顾性分析了2011年1月至2013年12月在我院接受根治性切除的I期肺腺癌患者。使用“PyRadiomics”软件包，从计算机断层扫描（CT）图像中提取88个影像组学特征，并使用朴素贝叶斯机器学习方法构建预测模型。通过受试者工作特征曲线分析评估模型的准确性，并在内部和外部对模型的性能进行验证。
共有233例患者纳入训练队列，其中69例（29.6%）患者为STAS（+）。STAS患者的无复发生存期和总生存期较差（P < 0.001）。特征提取后，选出5个最具贡献的影像组学特征来建立朴素贝叶斯模型。在内部验证中，该模型表现良好，曲线下面积值为0.63（0.55 - 0.71）。在一个包含112例患者的测试队列上进行外部验证，曲线下面积值为0.69。
基于CT的影像组学在术前预测I期肺腺癌的STAS方面具有价值，这可能有助于外科医生确定最佳手术方式。
