Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX. Lung cancer is the leading cause of cancer related death worldwide 1 . Early diagnosis of lung cancer is key to reducing mortality. Screening computed tomography (CT) examinations have been shown to greatly improve noninvasive early diagnosis of lung cancer in at risk patients 2,3 . The National Lung Screening Trial (NLST) has shown that low dose computed tomography screening could reduce patient mortality by 20% compared with conventional chest radiographs 4 . The US Preventive Services Task Force (USPSTF) recommends annual screening for lung cancer with computed tomography for high risk patients. Our ability to conduct lung nodule malignancy classification from clinical CT data has important clinical impacts. However, for current clinical evaluation, radiologist's interpretation is a tedious process, which is an impediment of clinical throughput, and highly subjective. It would be ideal to develop computational approaches, with performance comparable to radiologists, in order to aid or ease the burden of radiologists, and also potentially identify and make use of image features that may not be visually perceptible to even very experienced thoracic radiologists. There are several substantial challenges for computational approach development with currently available CT datasets: (i) Computational approaches need to provide sensitivity to nodule features as well as robustness to noise and other artifacts introduced during data collection. (ii) Datasets need to be large enough, consistent, and ensure the integrity of expert-defined ground "truth" for approach development, training, and testing 5 . The definition of "truth" and other statistical issues regarding database design for training computer-aided diagnosis tools are discussed in 6 . The development of a successful computational approach should take these challenges into consideration. Published: xx xx xxxx OPEN www.nature.com/scientificreports/
Causey et al. REF proposed a classification neural network based on deep learning features of a lung nodule in CT images.
Highly accurate model for prediction of lung nodule malignancy with CT scans
Scientific Reports
Scientific Reports
Computer Science Biology Mathematics Medicine