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Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network
| Content Provider | Scilit |
|---|---|
| Author | Xu, Yi-Ming Zhang, Teng Xu, Hai Qi, Liang Zhang, Wei Zhang, Yu-Dong Gao, Da-Shan Yuan, Mei Yu, Tong-Fu |
| Copyright Year | 2020 |
| Description | Journal: Cancer Management and Research 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. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196793/pdf https://www.dovepress.com/getfile.php?fileID=57725 |
| Ending Page | 2992 |
| Page Count | 14 |
| Starting Page | 2979 |
| ISSN | 11791322 |
| DOI | 10.2147/cmar.s239927 |
| Journal | Cancer Management and Research |
| Volume Number | ume 12 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-04-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Cancer Management and Research Respiratory System Computer-aided Detection Computed Tomography Pulmonary Nodules Convolutional Neural Network |
| Content Type | Text |
| Subject | Oncology |
