A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

Background: In this study we aimed to establish a new transfer learning model based on noncontrast and thin-layer com-puted tomography (CT) scans to distinguish between malignant and benign solid lung nodules. Material/Methods: CT images from 202 patients with 210 lesions (malignant: 127, benign: 83) manifesting as solid lung nodules from January 2016 to December 2020 from 3 institutions were retrospectively collected, and each nodule was histopathologically confirmed. Two experienced thoracic radiologists reviewed all images and determined the regions of interest (ROIs) in the three-dimensional (3D) images layer-by-layer. We divided the lesions and im-ages into training and testing sets at a ratio of 7: 3. The Inception V3 model was pretrained by the training da-taset. Five-fold cross-validation was used to choose the optimal model. Receiver operator characteristic curves (ROC curves) for methods to evaluate the performance of the models were drafted. Results: In the validation set, the AUC, accuracy, sensitivity, and specificity of Inception V3 model (lesion-level) were 0.999, 0.989, 0.983, and 1.0, respectively, which is higher than the image-level (0.997, 0.933, 0.922, and 0.948, respectively). The Inception V3 model (lesion-level) performed better than the image-level but there was no significant difference between the models (P>0.05). The ResNet50 model based on image level achieved AUC, accuracy, sensitivity, and specificity of 0.963, 0.926, 0.916, and 0.944, respectively, which is lower than that of Inception V3. Conclusions: Our study developed a novel deep learning model based on noncontrast and thin-layer CT scans to classify be-nign vs malignant lung nodules, and the Inception V3 model greatly improved the differentiation accuracy and specificity.