Literature DB >> 27521737

The American College of Radiology Lung Imaging Reporting and Data System: Potential Drawbacks and Need for Revision.

Hiren J Mehta1, Tan-Lucien Mohammed2, Michael A Jantz3.   

Abstract

Lung cancer screening using low-dose CT scanning reduces lung-cancer-specific and overall mortality in high-risk patients. A significant limitation of lung cancer screening is the false-positive rate. The American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS) was designed to standardize reporting of low-dose lung cancer screening results and to decrease the false-positive rate without significantly compromising sensitivity. Implementing Lung-RADS can also improve cost-effectiveness. However, Lung-RADS has never been studied in a prospective fashion. It also does not have a specific reporting category for patients with isolated hilar and mediastinal adenopathy or pleural effusion in the absence of lung nodules. We report four such cases from our lung cancer screening program. We believe that this is a significant limitation of Lung-RADS and should be revised in its new version.
Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Lung-RADS; lung cancer screening; mediastinal and hilar adenopathy

Mesh:

Year:  2016        PMID: 27521737     DOI: 10.1016/j.chest.2016.07.028

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  6 in total

1.  Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?

Authors:  Johanna Uthoff; Nicholas Koehn; Jared Larson; Samantha K N Dilger; Emily Hammond; Ann Schwartz; Brian Mullan; Rolando Sanchez; Richard M Hoffman; Jessica C Sieren
Journal:  Eur Radiol       Date:  2019-04-01       Impact factor: 5.315

2.  A K+-sensitive AND-gate dual-mode probe for simultaneous tumor imaging and malignancy identification.

Authors:  Qiyue Wang; Fangyuan Li; Zeyu Liang; Hongwei Liao; Bo Zhang; Peihua Lin; Xun Liu; Shen Hu; Jiyoung Lee; Daishun Ling
Journal:  Natl Sci Rev       Date:  2022-04-28       Impact factor: 23.178

3.  Protocol and Rationale for the International Lung Screening Trial.

Authors:  Kuan Pin Lim; Henry Marshall; Martin Tammemägi; Fraser Brims; Annette McWilliams; Emily Stone; Renee Manser; Karen Canfell; Marianne Weber; Luke Connelly; Rayleen V Bowman; Ian A Yang; Paul Fogarty; John Mayo; John Yee; Renelle Myers; Sukhinder Atkar-Khattra; David C L Lam; Antoni Rosell; Christine D Berg; Kwun M Fong; Stephen Lam
Journal:  Ann Am Thorac Soc       Date:  2020-04

Review 4.  Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.

Authors:  Carlos A Peña-Solórzano; David W Albrecht; Richard B Bassed; Michael D Burke; Matthew R Dimmock
Journal:  Forensic Sci Int       Date:  2020-10-18       Impact factor: 2.395

5.  Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening.

Authors:  Sebastian Ziegelmayer; Markus Graf; Marcus Makowski; Joshua Gawlitza; Felix Gassert
Journal:  Cancers (Basel)       Date:  2022-03-29       Impact factor: 6.639

Review 6.  Lung Cancer Screening: Review and 2021 Update.

Authors:  Anuradha Ramaswamy
Journal:  Curr Pulmonol Rep       Date:  2022-04-02
  6 in total

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