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The early identification of malignant pulmonary nodules is critical forbetter lung cancer prognosis and less invasive chemo or radio therapies. Nodulemalignancy assessment done by radiologists is extremely useful for planning apreventive intervention but is, unfortunately, a complex, time-consuming anderror-prone task. This explains the lack of large datasets containingradiologists malignancy characterization of nodules. In this article, wepropose to assess nodule malignancy through 3D convolutional neural networksand to integrate it in an automated end-to-end existing pipeline of lung cancerdetection. For training and testing purposes we used independent subsets of theLIDC dataset. Adding the probabilities of nodules malignity in a baseline lungcancer pipeline improved its F1-weighted score by 14.7%, whereas integratingthe malignancy model itself using transfer learning outperformed the baselineprediction by 11.8% of F1-weighted score. Despite the limited size of the lungcancer datasets, integrating predictive models of nodule malignancy improvesprediction of lung cancer.

Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline