Table 2. Artificial Intelligence studies related to pulmonary nodules management.
| Author | Objective | Algorithm | Application | Main results |
|---|---|---|---|---|
| Nam JG, et al. | To develop and validate a DLAD for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists | Deep learning-based automatic detection algorithm | Outperformance of radiograph classification and nodule detection for malignant pulmonary nodules on chest radiographs | Radiograph classification performances of DLAD were a range of 0.92–0.99 (AUROC) and 0.831–0.924 (JAFROC FOM), respectively |
| Li W, et al. | To design a deep convolutional neural networks method for nodule classification, with the advantage of autolearning representation and strong generalization ability | Deep convolutional neural networks | Pulmonary nodule recognition and classification | Results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods |
| Nibali A, et al. | To improve the ability of CAD systems to predict the malignancy of nodules from cropped CT images of lung nodules | Deep residual networks | Pulmonary nodule malignancy classification | The system achieves the highest performance in terms of all metrics measured including sensitivity, specificity, precision, AUROC, and accuracy |
| Eppenhof KAJ, et al. | To develop a deformable registration method based on a 3-D convolutional neural network, together with a framework for training such a network | Convolutional neural networks | Pulmonary CT registration | This approach results in an accurate and very fast deformable registration method, without a requirement for parameterization at test time or manually annotated data for training |
| da Silva GLF, et al. | To proposes a methodology to reduce the number of false positives using a deep learning technique in conjunction with an evolutionary technique | Convolutional neural networks | Lung nodule false positive reduction on CT images | The methodology was tested on CT scans with the highest accuracy of 97.62%, sensitivity of 92.20%, specificity of 98.64%, and AUROC curve of 0.955 |
| Naqi SM, et al. | To develop a multistage segmentation model to accurately extract nodules from lung CT images | Support vector machine | Lung nodule segmentation method | The classification is performed over GTFD feature vector, and the results show 99% accuracy, 98.6% sensitivity and 98.2% specificity with 3.4 false positives per scan |
| Choi W, et al. | To develop a radiomics prediction model to improve pulmonary nodule classification in low-dose CT, and to compare the model with the Lung-RADS for early detection of lung cancer | Support vector machine | Improvement of pulmonary nodule classification in low‐dose CT | The model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS |
| Bashir U, et al. | To compare the performance of random forest algorithms utilizing CT radiomics and/or semantic features in classifying NSCLC | Random forest | Non-invasive classification of non-small cell lung cancer | Non-invasive classification of NSCLC can be done accurately using random forest classification models based on well-known CT-derived descriptive features |
DLAD, deep learning-based automatic detection algorithm; CAD, computer-aided diagnosis; CT, computed tomography; AUROC, area under the receiver operating characteristic; GTFD, Geometric texture features descriptor; Lung-RADS, Lung CT Screening Reporting and Data System of the American College of Radiology; NSCLC, non-small cell lung cancer.