Artificial intelligence solution to classify pulmonary nodules on CT
D Blanc, Victor Racine, Antoine Khalil, M. Deloche, J.-A. Broyelle, I. Hammouamri, E. Sinitambirivoutin, M. Fiammante, E. Verdier, T. Besson, Alexandre Sadate, Mathieu Léderlin, François Laurent, Guillaume Chassagnon, G. Ferretti, Yann Diascorn, Pierre‐Yves Brillet, L. Cassagnes, Caroline Caramella, Antoine Loubet, N. Abassebay, Philippe Cuingnet, Mickaël Ohana, Julien Behr, Angeline Ginzac, Hugo Veyssière, Xavier Durando, Imad Bousaid, Nathalie Lassau, J Bréhant
Abstract
PURPOSE: or not, using machine learning and deep learning techniques. MATERIALS AND METHOD: The dataset used to train the model was provided by the organization team of the SFR (French Radiological Society) Data Challenge 2019. An asynchronous and parallel 3-stages pipeline was developed to process all the data (a data "pre-processing" stage; a "nodule detection" stage; a "classifier" stage). Lung segmentation was achieved using 3D U-NET algorithm; nodule detection was done using 3D Retina-UNET and classifier stage with a support vector machine algorithm on selected features. Performances were assessed using area under receiver operating characteristics curve (AUROC). RESULTS: The pipeline showed good performance for pathological nodule detection and patient diagnosis. With the preparation dataset, an AUROC of 0.9058 (95% confidence interval [CI]: 0.8746-0.9362) was obtained, 87% yielding accuracy (95% CI: 84.83%-91.03%) for the "nodule detection" stage, corresponding to 86% specificity (95% CI: 82%-92%) and 89% sensitivity (95% CI: 84.83%-91.03%). CONCLUSION: A fully functional pipeline using 3D U-NET, 3D Retina-UNET and classifier stage with a support vector machine algorithm was developed, resulting in high capabilities for pulmonary nodule classification.