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Front Oncol. 2018 Apr 16;8:108. doi: 10.3389/fonc.2018.00108. eCollection 2018.

Lung Nodule Detection via Deep Reinforcement Learning.

Author information

1
Department of Therapeutic Radiology, School of Medicine, Yale University, New Haven, CT, United States.
2
Department of Chronic Disease Epidemiology, School of Public Health, Yale University, New Haven, CT, United States.
3
Department of Radiology and Biomedical Imaging, School of Medicine, Yale University, New Haven, CT, United States.
4
Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States.

Abstract

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.

KEYWORDS:

computed tomography; computer-aided detection; lung cancer; lung nodules; reinforcement learning

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