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      Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

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          Abstract

          Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.

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          ImageNet classification with deep convolutional neural networks

          Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton (2017)
          Article 0 comments Cited 3401 times – based on 0 reviews     Review now
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            A survey on deep learning in medical image analysis

            Geert Litjens, Arnaud Arindra Adiyoso Setio, Thijs Kooi … (2017)
            Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.
            Article 0 comments Cited 1907 times – based on 0 reviews     Review now
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              Reduced lung-cancer mortality with low-dose computed tomographic screening.

              (2011)
              The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer. From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02). Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).
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                Author and article information

                Journal
                Journal ID (nlm-ta): Transl Lung Cancer Res
                Journal ID (iso-abbrev): Transl Lung Cancer Res
                Journal ID (publisher-id): TLCR
                Title: Translational Lung Cancer Research
                Publisher: AME Publishing Company
                ISSN (Print): 2218-6751
                ISSN (Electronic): 2226-4477
                Publication date (Print and electronic): May 2021
                Publication date (Print): May 2021
                Volume: 10
                Issue: 5
                Pages: 2378-2388
                Affiliations
                [1 ]Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc , Nijmegen, The Netherlands;
                [2 ]Fraunhofer MEVIS , Bremen, Germany
                Author notes

                Contributions: (I) Conception and design: All authors; (II) Administrative support: None; (III) Provision of study materials or patients: None; (IV) Collection and assembly of data: None; (V) Data analysis and interpretation: None; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                Correspondence to: Colin Jacobs. Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands. Email: colin.jacobs@ 123456radboudumc.nl .
                Article
                Publisher ID: tlcr-10-05-2378
                DOI: 10.21037/tlcr-2020-lcs-06
                PMC ID: 8182724
                PubMed ID: 34164285
                SO-VID: 477fab96-6b3f-450a-b13f-1b20585cfc2b
                Copyright © 2021 Translational Lung Cancer Research. All rights reserved.
                License:

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

                History
                Date received : 26 February 2020
                Date accepted : 18 June 2020
                Categories
                Subject: Review Article on Lung Cancer Screening

                Keywords: lung cancer,artificial intelligence (ai),computed tomography (ct),pulmonary nodule
                Data availability:
                Keywords: lung cancer, artificial intelligence (ai), computed tomography (ct), pulmonary nodule

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