Abstract
The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.
Highlights
Lung cancer is still the leading cause of cancer-related deaths in both the United States [1] and Europe, where it accounts for 20.9% of all cancer-related deaths [2]
When chest radiographs are replaced by computed tomography (CT) scans for pulmonary cancer assessment, there will inevitably be an increase in workload for the radiologists, which results in missed cases and errors in diagnostics [6,7]
The terms were combined with following text words in the title and/or abstract: “lung”, “pulmonary”, “respiratory”, “classification”, “characterization”, “detection”, “artificial intelligence”, “machine learning”, “deep learning”, “neural network”, “computer-assisted”, “computer-aided”, “CT”, and “computed tomography”
Summary
Lung cancer is still the leading cause of cancer-related deaths in both the United States [1] and Europe, where it accounts for 20.9% of all cancer-related deaths [2]. Much has been done with prevention, there are still around 370,000 new cases of lung cancer each year [2]. It is crucial to diagnose lung cancer at an early stage to increase patients’ chance of survival. Efforts to detect lung cancer through imaging were widely investigated, and no significant reduction in mortality by screening with traditional chest radiography was reported [3,4]. Since computed tomography (CT) has emerged as an imaging method with superior sensitivity in detecting lung nodules, and screening with CT has been shown to be superior to traditional chest radiography in reducing mortality from lung cancer [5]. When chest radiographs are replaced by CT scans for pulmonary cancer assessment, there will inevitably be an increase in workload for the radiologists, which results in missed cases and errors in diagnostics [6,7]
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