{Reference Type}: Journal Article
{Title}: Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.
{Author}: Shimada Y;Ojima T;Takaoka Y;Sugano A;Someya Y;Hirabayashi K;Homma T;Kitamura N;Akemoto Y;Tanabe K;Sato F;Yoshimura N;Tsuchiya T;
{Journal}: Surg Today
{Volume}: 54
{Issue}: 6
{Year}: 2024 Jun 20
{Factor}: 2.54
{DOI}: 10.1007/s00595-023-02756-z
{Abstract}: <B>OBJECTIVE: </B>To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.<BR><B>METHODS: </B>Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients.<BR><B>RESULTS: </B>The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons.<BR><B>CONCLUSIONS: </B>The deep learning model systems can be utilized in clinical applications via data expansion.