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Computed tomography (CT) examinations are commonly used to predict lungnodule malignancy in patients, which are shown to improve noninvasive earlydiagnosis of lung cancer. It remains challenging for computational approachesto achieve performance comparable to experienced radiologists. Here we presentNoduleX, a systematic approach to predict lung nodule malignancy from CT data,based on deep learning convolutional neural networks (CNN). For training andvalidation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort.All nodules were identified and classified by four experienced thoracicradiologists who participated in the LIDC project. NoduleX achieves highaccuracy for nodule malignancy classification, with an AUC of ~0.99. This iscommensurate with the analysis of the dataset by experienced radiologists. Ourapproach, NoduleX, provides an effective framework for highly accurate nodulemalignancy prediction with the model trained on a large patient population. Ourresults are replicable with software available athttp://bioinformatics.astate.edu/NoduleX.

Highly accurate model for prediction of lung nodule malignancy with CT scans