Li Huanhuan, Gao Long, Ma He, Arefan Dooman, He Jiachuan, Wang Jiaqi, Liu Hu
Department of Radiology, The First Hospital of China Medical University, Shenyang, China.
College of Computer, National University of Defense Technology, Changsha, China.
Front Oncol. 2021 Apr 29;11:658887. doi: 10.3389/fonc.2021.658887. eCollection 2021.
To evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images.
A total of 200 patients with radiologically defined central lung cancer were recruited. All patients underwent dual-phase chest CECT, and the histological subtypes (adenocarcinoma (ADC), squamous cell carcinoma (SCC), small cell lung cancer (SCLC)) were confirmed by histopathological samples. 107 features were used in five machine learning classifiers to perform the predictive analysis among three subtypes. Models were trained and validated in two conditions: using radiomic features alone, and combining clinical features with radiomic features. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC).
The highest AUCs in classifying ADC vs. SCC, ADC vs. SCLC, and SCC vs. SCLC were 0.879, 0.836, 0.783, respectively by using only radiomic features in a feedforward neural network.
Our study indicates that radiomic features based on the CECT images might be a promising tool for noninvasive prediction of histological subtypes in central lung cancer and the neural network classifier might be well-suited to this task.
评估在对比增强CT（CECT）图像中，放射组学特征对中央型肺癌组织学亚型分类的有效性。
共招募了200例经影像学确诊的中央型肺癌患者。所有患者均接受了双期胸部CECT检查，组织学亚型（腺癌（ADC）、鳞状细胞癌（SCC）、小细胞肺癌（SCLC））通过组织病理学样本得以确认。在五个机器学习分类器中使用107个特征对三种亚型进行预测分析。模型在两种情况下进行训练和验证：仅使用放射组学特征，以及将临床特征与放射组学特征相结合。分类模型的性能通过受试者操作特征曲线（AUC）下的面积进行评估。
在前馈神经网络中仅使用放射组学特征时，在区分ADC与SCC、ADC与SCLC以及SCC与SCLC时的最高AUC分别为0.879、0.836、0.783。
我们的研究表明，基于CECT图像的放射组学特征可能是一种用于中央型肺癌组织学亚型无创预测的有前景的工具，并且神经网络分类器可能非常适合这项任务。
