A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets

Lung cancer is the most common fatal malignancy in adults worldwide, and non-small-cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of patients with NSCLC. We trained and evaluated LungNet on four independent cohorts of patients with NSCLC from four medical centres: Stanford Hospital (n=129), H. Lee Moffitt Cancer Center and Research Institute (n=185), MAASTRO Clinic (n=311) and Charite - Universitatsmedizin, Berlin (n=84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3 and 4, respectively. Furthermore, the survival model can be used, via transfer learning, for classifying benign versus malignant nodules on the Lung Image Database Consortium (n=1,010), with improved performance (AUC=0.85) versus training from scratch (AUC=0.82). LungNet can be used as a non-invasive predictor for prognosis in patients with NSCLC and can facilitate interpretation of computed tomography images for lung cancer stratification and prognostication. Predicting overall survival for patients with confirmed non-small-cell lung cancer is an important issue in clinical practice. The authors developed and validated in four independent patient cohorts a shallow convolutional neural network that can predict the outcomes of individuals using pre-treatment CT images. The authors further show that the survival model can be used, via transfer learning, for classifying benign versus malignant nodules.