Department of Radiology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China.
Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA.
Acad Radiol. 2019 Sep;26(9):1245-1252. doi: 10.1016/j.acra.2018.10.013. Epub 2018 Nov 28.
To evaluate the performance of using radiomics method to classify lung cancer histological subtypes based on nonenhanced computed tomography images.
278 patients with pathologically confirmed lung cancer were collected, including 181 nonsmall cell lung cancer (NSCLC) and 97 small cell lung cancers (SCLC) patients. Among the NSCLC patients, 88 patients were adenocarcinomas (AD) and 93 patients were squamous cell carcinomas (SCC). In total, 1695 quantitative radiomic features (QRF) were calculated from the primary lung cancer tumor in each patient. To build radiomic classification model based on the extracted QRFs, several machine-learning algorithms were applied sequentially. First, unsupervised hierarchical clustering was used to exclude highly correlated QRFs; second, the minimum Redundancy Maximum Relevance feature selection algorithm was employed to select informative and nonredundant QRFs; finally, the Incremental Forward Search and Support Vector Machine classification algorithms were used to combine the selected QRFs and build the model. In our work, to study the phenotypic differences among lung cancer histological subtypes, four classification models were built. They were models of SCLC vs NSCLC, SCLC vs AD, SCLC vs SCC, and AD vs SCC. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC) estimated by three-fold cross-validation.
The AUC (95% confidence interval) for the model of SCLC vs NSCLC was 0.741(0.678, 0.795). For the models of SCLC vs AD and SCLC vs SCC, the AUCs were 0.822(0.755, 0.875) and 0.665(0.583, 0.738), respectively. The AUC for the model of AD vs SCC was 0.655(0.570, 0.731). Several QRFs ("Law_15," "LoG_Uniformity," "GLCM_Contrast," and "Compactness Factor") that characterize tumor heterogeneity and shape were selected as the significant features to build the models.
Our results show that phenotypic differences exist among different lung cancer histological subtypes on nonenhanced computed tomography image.
评估基于非增强 CT 图像的放射组学方法对肺癌组织学亚型进行分类的性能。
共收集 278 例经病理证实的肺癌患者，包括 181 例非小细胞肺癌（NSCLC）和 97 例小细胞肺癌（SCLC）患者。其中 NSCLC 患者中腺癌（AD）88 例，鳞癌（SCC）93 例。共计算了每位患者原发性肺癌肿瘤的 1695 个定量放射组学特征（QRF）。为了基于提取的 QRF 构建放射组学分类模型，依次应用了几种机器学习算法。首先，采用无监督层次聚类排除高度相关的 QRF；其次，采用最小冗余最大相关性特征选择算法选择信息丰富且非冗余的 QRF；最后，采用增量前向搜索和支持向量机分类算法结合选择的 QRF 并构建模型。在我们的工作中，为了研究肺癌组织学亚型之间的表型差异，构建了 4 个分类模型，即 SCLC 与 NSCLC、SCLC 与 AD、SCLC 与 SCC 和 AD 与 SCC。通过三折交叉验证估计的受试者工作特征曲线下面积（AUC）评估分类模型的性能。
SCLC 与 NSCLC 模型的 AUC（95%置信区间）为 0.741（0.678，0.795）。SCLC 与 AD 和 SCLC 与 SCC 模型的 AUC 分别为 0.822（0.755，0.875）和 0.665（0.583，0.738），AD 与 SCC 模型的 AUC 为 0.655（0.570，0.731）。几个特征（“Law_15”“LoG_Uniformity”“GLCM_Contrast”和“Compactness Factor”）被选为特征以构建模型，这些特征可以描述肿瘤异质性和形状。
我们的结果表明，不同肺癌组织学亚型在非增强 CT 图像上存在表型差异。
