Lee Jong Eun, Do Luu Ngoc, Jeong Won Gi, Lee Hyo Jae, Chae Kum Ju, Kim Yun Hyeon, Park Ilwoo
Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea.
Department of Radiology, Chonnam National University, Gwangju, Korea.
J Pers Med. 2022 Nov 7;12(11):1859. doi: 10.3390/jpm12111859.
This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN).
In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets. The data from the second and third institutions were used as an external testing dataset. Radiomic features were extracted from the intra and perinodular regions of interest (ROI). After a feature selection process, Support vector machine (SVM) was used to train models for classifying between LC and LM. The performances of the SVM classifiers were evaluated with both the internal and external testing datasets. The performances of the model were compared to those of two radiologists who reviewed the CT images of the testing datasets for the binary prediction of LC versus LM.
The SVM classifier trained with the radiomic features from the intranodular ROI and achieved the sensitivity/specificity of 0.545/0.828 in the internal test dataset, and 0.833/0.964 in the external test dataset, respectively. The SVM classifier trained with the combined radiomic features from the intra- and perinodular ROIs achieved the sensitivity/specificity of 0.545/0.966 in the internal test dataset, and 0.833/1.000 in the external test data set, respectively. Two radiologists demonstrated the sensitivity/specificity of 0.545/0.966 and 0.636/0.828 in the internal test dataset, and 0.917/0.929 and 0.833/0.929 in the external test dataset, which were comparable to the performance of the model trained with the combined radiomics features.
Our results suggested that the machine learning classifiers trained using radiomics features of SPN in CRC patients can be used to distinguish the primary LC and the solitary LM with a similar level of performance to radiologists.
本研究采用放射组学方法结合机器学习算法，以区分患有孤立性肺结节（SPN）的结直肠癌（CRC）患者的原发性肺癌（LC）和孤立性肺转移瘤（LM）。
在一项回顾性研究中，纳入了2011年至2019年间在三个不同机构接受胸部计算机断层扫描（CT）并被诊断为原发性LC或孤立性LM的239例患者。来自第一个机构的数据被分为训练数据集和内部测试数据集。来自第二个和第三个机构的数据用作外部测试数据集。从感兴趣的结节内和结节周围区域（ROI）提取放射组学特征。经过特征选择过程后，使用支持向量机（SVM）训练用于区分LC和LM的模型。使用内部和外部测试数据集评估SVM分类器的性能。将该模型的性能与两位放射科医生的性能进行比较，这两位医生对测试数据集的CT图像进行了LC与LM的二元预测评估。
使用来自结节内ROI的放射组学特征训练的SVM分类器在内部测试数据集中的灵敏度/特异性分别为0.545/0.828，在外部测试数据集中为0.833/0.964。使用来自结节内和结节周围ROI的组合放射组学特征训练的SVM分类器在内部测试数据集中的灵敏度/特异性分别为0.545/0.966，在外部测试数据集中为0.833/1.000。两位放射科医生在内部测试数据集中的灵敏度/特异性分别为0.545/0.966和0.636/0.828，在外部测试数据集中为0.917/0.929和0.833/0.929，这与使用组合放射组学特征训练的模型的性能相当。
我们的结果表明，使用CRC患者SPN的放射组学特征训练的机器学习分类器可用于区分原发性LC和孤立性LM，其性能水平与放射科医生相似。
