Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.

OBJECTIVE. The purpose of this study was to assess, by analyzing features of the primary tumor with 18F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung cancer (NSCLC) to metastasize to lymph nodes or distant sites. MATERIALS AND METHODS. Consecutively registered patients with newly diagnosed, untreated NSCLC were retrospectively included in a single-center study. PET images were segmented with local image features extraction software, and data were used for CNN training and validation after data augmentation strategies were used. The standard of reference for designation of N category was invasive lymph node sampling or 6-month follow-up imaging. Distant metastases developing during the study follow-up period were assessed by imaging (CT or PET/CT), in tissue obtained from new suspected sites of disease, and according to the treating oncologist's designation. RESULTS. A total of 264 patients with NSCLC participated in follow-up for a median of 25.2 months (range, 6-43 months). N category designations were available for 223 of 264 (84.5%) patients, and M category for all 264. The sensitivity, specificity, and accuracy of CNN for predicting node positivity were 0.74 ± 0.32, 0.84 ± 0.16, and 0.80 ± 0.17. The corresponding values for predicting distant metastases were 0.45 ± 0.08, 0.79 ± 0.06, and 0.63 ± 0.05. CONCLUSION. This study showed that using a CNN to analyze segmented PET images of patients with previously untreated NSCLC can yield moderately high accuracy for designation of N category, although this may be insufficient to preclude invasive lymph node sampling. The sensitivity of the CNN in predicting distant metastases is fairly poor, although specificity is moderately high.