Kaiqiang Yang1, Jinsha Liu2, Wen Tang3, Huiling Zhang3, Rongguo Zhang3, Jun Gu3, Ruiping Zhu4, Jingtong Xiong5, Xiaoshuang Ru6, Jianlin Wu7. 1. Department of Radiology, Zhongshan Hospital, Dalian University, Dalian, Liaoning Province, China; Infervision, Beijing, China. 2. Department of Radiology, Zhongshan Hospital, Dalian University, Dalian, Liaoning Province, China. 3. Infervision, Beijing, China. 4. Department of Pathology, Zhongshan Hospital, Dalian University, Dalian, Liaoning Province, China. 5. Second Hospital, Dalian Medical University, Dalian, Liaoning Province, China. 6. Central Hospital, Dalian, Liaoning Province, China. 7. Department of Radiology, Zhongshan Hospital, Dalian University, Dalian, Liaoning Province, China. Electronic address: cjr.wujianlin@vip.163.com.
Abstract
PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Networks (CapNets) and provide a reference for the early diagnosis of lung cancer. METHOD: The dataset consisted of 1177 samples (benign/malignant: 414/763) from 997 patients provided by collaborating hospital. All nodules were biopsy or surgery proven, and pathologic results were regarded as the "golden standard". This study utilized partial U-Net to capture the low-level (edge, corner, etc.) information and CapNets to preserve high-level (semantic information) information of nodules. For CapNets, each capsule had a 4 × 4 matrix representing the pose and an activation probability representing the presence of an object. Furthermore, we chose accuracy (ACC), area under the curve (AUC), sensitivity (SE) and specificity (SP) to evaluate the generalization of the proposed architecture and compared its identification performance with 3D U-Net and experienced radiologists. RESULTS: The AUC of our architecture (0.84) was superior to that (0.81) of the original 3D U-Net (p = 0.04, DeLong's test). Moreover, ACC (84.5 %) and SE (92.9 %) of our model were clearly higher than radiologists' ACC (81.0 %) and SE (84.3 %) at the optimal operating point. However, SP (70 %) of our model was slightly lower than radiologists' SP (75 %), which might be the result of class imbalance with limited benign samples involved for algorithm training. CONCLUSIONS: Our architecture showed a high performance for identifying benign and malignant pulmonary nodules, indicating the improved model has a promising application in clinic.
PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Networks (CapNets) and provide a reference for the early diagnosis of lung cancer. METHOD: The dataset consisted of 1177 samples (benign/malignant: 414/763) from 997 patients provided by collaborating hospital. All nodules were biopsy or surgery proven, and pathologic results were regarded as the "golden standard". This study utilized partial U-Net to capture the low-level (edge, corner, etc.) information and CapNets to preserve high-level (semantic information) information of nodules. For CapNets, each capsule had a 4 × 4 matrix representing the pose and an activation probability representing the presence of an object. Furthermore, we chose accuracy (ACC), area under the curve (AUC), sensitivity (SE) and specificity (SP) to evaluate the generalization of the proposed architecture and compared its identification performance with 3D U-Net and experienced radiologists. RESULTS: The AUC of our architecture (0.84) was superior to that (0.81) of the original 3D U-Net (p = 0.04, DeLong's test). Moreover, ACC (84.5 %) and SE (92.9 %) of our model were clearly higher than radiologists' ACC (81.0 %) and SE (84.3 %) at the optimal operating point. However, SP (70 %) of our model was slightly lower than radiologists' SP (75 %), which might be the result of class imbalance with limited benign samples involved for algorithm training. CONCLUSIONS: Our architecture showed a high performance for identifying benign and malignant pulmonary nodules, indicating the improved model has a promising application in clinic.
Authors: Kaiyue Diao; Yuntian Chen; Ying Liu; Bo-Jiang Chen; Wan-Jiang Li; Lin Zhang; Ya-Li Qu; Tong Zhang; Yun Zhang; Min Wu; Kang Li; Bin Song Journal: Ann Transl Med Date: 2022-06