Xiaofeng Lin1, Han Jiao2, Zhiyong Pang2, Huai Chen3, Weijie Wu1, Xiaoyi Wang1, Lang Xiong1, Biyun Chen1, Yihua Huang2, Sheng Li1, Li Li4. 1. Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China. 2. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, People's Republic of China. 3. Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China. 4. Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China. Electronic address: li2@mail.sysu.edu.cn.
Abstract
BACKGROUND: We aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patients with spiculation, lobulation, or pleural indentation on CT. PATIENTS AND METHODS: We retrospectively recruited 915 patients with solitary solid pulmonary nodules and suspicious signs of malignancy. Data including clinical characteristics and subjective CT findings were obtained. A 3-dimensional U-Net-based DL model was used for tumor segmentation and extraction of 3-dimensional radiomics features. We used the Maximum Relevance and Minimum Redundancy (mRMR) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm to select the intranodular, perinodular, and gross nodular radiomics features. We propose a medical image DL (IDL) model, a clinical image DL (CIDL) model, a radiomics DL (RDL) model, and a clinical image radiomics DL (CIRDL) model to preoperatively differentiate GNs from solid lung cancer. Five-fold cross-validation was used to select and evaluate the models. The prediction performance of the models was evaluated using receiver operating characteristic and calibration curves. RESULTS: The CIRDL model achieved the best performance in differentiating between GNs and solid lung cancer (area under the curve [AUC] = 0.9069), which was significantly higher compared with the IDL (AUC = 0.8322), CIDL (AUC = 0.8652), intra-RDL (AUC = 0.8583), peri-RDL (AUC = 0.8259), and gross-RDL (AUC = 0.8705) models. CONCLUSION: The proposed CIRDL model is a noninvasive diagnostic tool to differentiate between granuloma nodules and solid lung cancer nodules and reduce the need for invasive diagnostic and surgical procedures.
BACKGROUND: We aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patients with spiculation, lobulation, or pleural indentation on CT. PATIENTS AND METHODS: We retrospectively recruited 915 patients with solitary solid pulmonary nodules and suspicious signs of malignancy. Data including clinical characteristics and subjective CT findings were obtained. A 3-dimensional U-Net-based DL model was used for tumor segmentation and extraction of 3-dimensional radiomics features. We used the Maximum Relevance and Minimum Redundancy (mRMR) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm to select the intranodular, perinodular, and gross nodular radiomics features. We propose a medical image DL (IDL) model, a clinical image DL (CIDL) model, a radiomics DL (RDL) model, and a clinical image radiomics DL (CIRDL) model to preoperatively differentiate GNs from solid lung cancer. Five-fold cross-validation was used to select and evaluate the models. The prediction performance of the models was evaluated using receiver operating characteristic and calibration curves. RESULTS: The CIRDL model achieved the best performance in differentiating between GNs and solid lung cancer (area under the curve [AUC] = 0.9069), which was significantly higher compared with the IDL (AUC = 0.8322), CIDL (AUC = 0.8652), intra-RDL (AUC = 0.8583), peri-RDL (AUC = 0.8259), and gross-RDL (AUC = 0.8705) models. CONCLUSION: The proposed CIRDL model is a noninvasive diagnostic tool to differentiate between granuloma nodules and solid lung cancer nodules and reduce the need for invasive diagnostic and surgical procedures.