Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea.
Department of Medical Statistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Eur Radiol. 2020 Sep;30(9):4883-4892. doi: 10.1007/s00330-020-06805-w. Epub 2020 Apr 16.
To develop a model for differentiating the predominant subtype-based prognostic groups of lung adenocarcinoma using CT radiomic features, and to validate its performance in comparison with radiologists' assessments.
A total of 993 patients presenting with invasive lung adenocarcinoma between March 2010 and June 2016 were identified. Predominant histologic subtypes were categorized into three groups according to their prognosis (group 0: lepidic; group 1: acinar/papillary; group 2: solid/micropapillary). Seven hundred eighteen radiomic features were extracted from segmented lung cancers on contrast-enhanced CT. A model-development set was formed from the images of 893 patients, while 100 image sets were reserved for testing. A least absolute shrinkage and selection operator method was used for feature selection. Performance of the radiomic model was evaluated using receiver operating characteristic curve analysis, and accuracy on the test set was compared with that of three radiologists with varying experiences (6, 7, and 19 years in chest CT).
Our model differentiated the three groups with areas under the curve (AUCs) of 0.892 and 0.895 on the development and test sets, respectively. In pairwise discrimination, the AUC was highest for group 0 vs. 2 (0.984). The accuracy of the model on the test set was higher than the averaged accuracy of the three radiologists without statistical significance (73.0% vs. 61.7%, p = 0.059). For group 2, the model achieved higher PPV than the observers (85.7% vs. 35.0-48.4%).
Predominant subtype-based prognostic groups of lung adenocarcinoma were classified by a CT-based radiomic model with comparable performance to radiologists.
• A CT-based radiomic model differentiated three prognosis-based subtype groups of lung adenocarcinoma with areas under the curve (AUCs) of 0.892 and 0.895 on development and test sets, respectively. • The CT-based radiomic model showed near perfect discrimination between group 0 and group 2 (AUCs, 0.984-1.000). • The accuracy of the CT-based radiomic model was comparable to the averaged accuracy of the three radiologists with 6, 7, and 19 years of clinical experience in chest CT (73.0% vs. 61.7%, p = 0.059), achieving a higher positive predictive value for group 2 than the observers (85.7% vs. 35.0-48.4%).
利用 CT 放射组学特征建立一种区分肺腺癌主要亚型预后组的模型，并与放射科医生的评估结果进行比较，验证其性能。
回顾性分析 2010 年 3 月至 2016 年 6 月期间经病理证实的 993 例浸润性肺腺癌患者的临床资料。根据其预后将主要组织学亚型分为三组（组 0：贴壁型；组 1：腺泡/乳头型；组 2：实体/微乳头型）。从增强 CT 扫描的肺癌分割图像中提取 718 个放射组学特征。采用最小绝对收缩和选择算子法进行特征选择。使用受试者工作特征曲线分析评估放射组学模型的性能，并将测试集的准确性与三位具有不同经验（胸部 CT 经验分别为 6、7 和 19 年）的放射科医生的准确性进行比较。
我们的模型在开发集和测试集上区分三组的曲线下面积（AUC）分别为 0.892 和 0.895。在两两比较中，组 0 与组 2 的 AUC 最高（0.984）。该模型在测试集上的准确性高于三位放射科医生的平均准确性，但无统计学意义（73.0% vs. 61.7%，p=0.059）。对于组 2，模型的阳性预测值（PPV）高于观察者（85.7% vs. 35.0-48.4%）。
基于 CT 的放射组学模型可对肺腺癌的主要基于亚型的预后组进行分类，其性能与放射科医生相当。
• 基于 CT 的放射组学模型可区分肺腺癌三种基于预后的亚型组，其在开发集和测试集上的 AUC 分别为 0.892 和 0.895。• CT 基放射组学模型在组 0 与组 2 之间具有近乎完美的区分度（AUCs，0.984-1.000）。• CT 基放射组学模型的准确性与具有 6、7 和 19 年胸部 CT 临床经验的三位放射科医生的平均准确性相当（73.0% vs. 61.7%，p=0.059），对于组 2，其阳性预测值高于观察者（85.7% vs. 35.0-48.4%）。
