Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network
| Autor: | Tong-Fu Yu, Yi-Ming Xu, Mei Yuan, Yu-Dong Zhang, Da-Shan Gao, Wei Zhang, Hai Xu, Liang Qi, Teng Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty Training set Receiver operating characteristic medicine.diagnostic_test business.industry Deep learning CAD Computed tomography Convolutional neural network 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Oncology 030220 oncology & carcinogenesis Pulmonary nodule medicine False positive paradox Radiology Artificial intelligence business |
| Zdroj: | Cancer Management and Research. 12:2979-2992 |
| ISSN: | 1179-1322 |
| DOI: | 10.2147/cmar.s239927 |
| Popis: | Purpose The purpose of this study is to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience in detecting pulmonary nodules on thin-section computed tomography (CT). Patients and methods We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution. The 3D CNN model for nodule detection was re-trained and complemented by expert augmentation. The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group. Results The detection performance of the re-trained CAD model was significantly better than that of the pre-trained network (sensitivity: 93.09% vs 38.44%). The re-trained CAD model had a significantly better detection performance than radiologists (average sensitivity: 93.09% vs 50.22%), without significantly increasing the number of false positives per scan (1.64 vs 0.68). In the training set, 922 nodules less than 3 mm in size in 211 patients at high risk were recommended for follow-up CT according to the Fleischner Society Guidelines. Fifteen of 101 solid nodules were confirmed to be lung cancer. Conclusion The re-trained 3D CNN-based CAD model, complemented by expert augmentation, was an accurate and efficient tool in identifying incidental pulmonary nodules for subsequent management. |
| Databáze: | OpenAIRE |
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