Literature DB >> 29053407

Lung-RADS: Pushing the Limits.

Maria D Martin1, Jeffrey P Kanne1, Lynn S Broderick1, Ella A Kazerooni1, Cristopher A Meyer1.   

Abstract

In response to the recommendation of the U.S. Preventive Services Task Force and the coverage decision by the Centers for Medicare and Medicaid Services for lung cancer screening (LCS) computed tomography (CT), the American College of Radiology introduced the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screening-detected lung nodules. As with many first-edition guidelines, questions arise when such reporting systems are used in daily practice. In this article, a collection of 15 LCS-related scenarios are presented that address situations in which the Lung-RADS guidelines are unclear or situations that are not currently addressed in the Lung-RADS guidelines. For these 15 scenarios, the authors of this article provide the reader with recommendations that are based on their collective experiences, with the hope that future versions of Lung-RADS will provide additional guidance, particularly as more data from widespread LCS are collected and analyzed. ©RSNA, 2017.

Entities:  

Mesh:

Year:  2017        PMID: 29053407     DOI: 10.1148/rg.2017170051

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  15 in total

1.  Insights for Management of Ground-Glass Opacities From the National Lung Screening Trial.

Authors:  Hilary A Robbins; Hormuzd A Katki; Li C Cheung; Rebecca Landy; Christine D Berg
Journal:  J Thorac Oncol       Date:  2019-05-22       Impact factor: 15.609

2.  Harnessing Machine Learning to Improve Patient Outcomes in Pulmonary and Critical Care Medicine.

Authors:  Anne R Levenson; Luisa Morales-Nebreda; Michael J Alexander; Clara J Schroedl
Journal:  Am J Respir Crit Care Med       Date:  2020-10-01       Impact factor: 21.405

Review 3.  Emerging Approaches to Complement Low-Dose Computerized Tomography for Lung Cancer Screening: A Narrative Review.

Authors:  Bradley Maller; Tawee Tanvetyanon
Journal:  Cureus       Date:  2022-07-26

4.  A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT.

Authors:  Andrew V Kossenkov; Rehman Qureshi; Noor B Dawany; Jayamanna Wickramasinghe; Qin Liu; R Sonali Majumdar; Celia Chang; Sandy Widura; Trisha Kumar; Wen-Hwai Horng; Eric Konnisto; Gerard Criner; Jun-Chieh J Tsay; Harvey Pass; Sai Yendamuri; Anil Vachani; Thomas Bauer; Brian Nam; William N Rom; Michael K Showe; Louise C Showe
Journal:  Cancer Res       Date:  2018-11-28       Impact factor: 12.701

5.  A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules.

Authors:  Mark M Hammer; Lauren L Palazzo; Andrew L Eckel; Eduardo M Barbosa; Chung Yin Kong
Journal:  Radiology       Date:  2018-11-20       Impact factor: 11.105

6.  External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis.

Authors:  Noemi Garau; Chiara Paganelli; Paul Summers; Wookjin Choi; Sadegh Alam; Wei Lu; Cristiana Fanciullo; Massimo Bellomi; Guido Baroni; Cristiano Rampinelli
Journal:  Med Phys       Date:  2020-06-23       Impact factor: 4.071

Review 7.  Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML).

Authors:  Rima Hajjo; Dima A Sabbah; Sanaa K Bardaweel; Alexander Tropsha
Journal:  Diagnostics (Basel)       Date:  2021-04-21

Review 8.  The Role of Imaging in Health Screening: Screening for Specific Conditions.

Authors:  David H Ballard; Kirsteen R Burton; Nikita Lakomkin; Shannon Kim; Prabhakar Rajiah; Midhir J Patel; Parisa Mazaheri; Gary J Whitman
Journal:  Acad Radiol       Date:  2020-05-11       Impact factor: 3.173

9.  Quantification of Minimum Detectable Difference in Radiomics Features Across Lesions and CT Imaging Conditions.

Authors:  Jocelyn Hoye; Justin B Solomon; Thomas J Sauer; Ehsan Samei
Journal:  Acad Radiol       Date:  2020-08-20       Impact factor: 5.482

10.  A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.

Authors:  Ayşegül Gürsoy Çoruh; Bülent Yenigün; Çağlar Uzun; Yusuf Kahya; Emre Utkan Büyükceran; Atilla Elhan; Kaan Orhan; Ayten Kayı Cangır
Journal:  Br J Radiol       Date:  2021-06-11       Impact factor: 3.629

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