Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram

作者：Ailing Liu, Zhiheng Wang, Yachao Yang, Jingtao Wang, Xiaoyu Dai, Lijie Wang, Yuan Lu, Fuzhong Xue · 发表于：癌症：英文版 · 年份：2020 · DOI：10.1002/cac2.12002 · 被引用次数：115 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、MRI in cancer diagnosis

BACKGROUND: Lung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules. METHODS: A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts. RESULTS: A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the tr...