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  • 2019-01-09

Towards radiologist-level malignancy detection on chest CT scans: a comparative study of the performance of convolutional neural networks and four thoracic radiologists

The demonstration of a 20% reduction in lung cancer mortality in the USA National Lung Screening Trial (NLST)[1] and the subsequent decision by the US Centers for Medicare and Medicaid Services to provide Medicare coverage for lung cancer screening has paved the way for nationwide lung cancer screening in the USA. Additionally, results of the NELSON trial also confirmed the value of low-dose CT screening with decreased mortality by 26% in high-risk men and 61% in high-risk women over a 10-year period.[2] Lung cancer screening programs have subsequently initiated big data analysis projects on chest CTs. The National Lung Screening Trial (NLST) dataset, in particular, has longitudinal data of high-risk patients to closely monitor potentially malignant lung nodules and provide the opportunity for the development of Computer-aided Detection (CADe) in detecting lung nodules and Computer-aided Diagnosis (CADx) systems in characterizing lung nodules to assist radiologists in reporting high volumes of chest CT scans.

Link to complete publication here: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=dqpMNRUAAAAJ&cstart=20&pagesize=80&citation_for_view=dqpMNRUAAAAJ:UebtZRa9Y70C

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