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Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening.

作者信息

Dyer Spencer C, Bartholmai Brian J, Koo Chi Wan

机构信息

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

出版信息

J Thorac Dis. 2020 Nov;12(11):6966-6977. doi: 10.21037/jtd-2019-cptn-02.


DOI:10.21037/jtd-2019-cptn-02
PMID:33282402
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7711402/
Abstract

Lung cancer remains the leading cause of cancer death in the United States. Screening with low-dose computed tomography (LDCT) has been proven to aid in early detection of lung cancer and reduce disease specific mortality. In 2014, the American College of Radiology (ACR) released version 1.0 of the Lung CT Screening Reporting and Data System (Lung-RADS) as a quality tool to standardize the reporting of lung cancer screening LDCT. In 2019, 5 years after the implementation of Lung-RADS version 1.0 the ACR released the updated Lung-RADS version 1.1 which incorporates initial experience with lung cancer screening. In this review, we outline the implications of the changes and additions in Lung-RADS version 1.1 and examine relevant literature for many of the updates. We also highlight several challenges and opportunities as Lung-RADS version 1.1 is implemented in lung cancer screening programs.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/0ddbff33943f/jtd-12-11-6966-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/f8d2f1db5e51/jtd-12-11-6966-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/93ea1cbaedfb/jtd-12-11-6966-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/fa63dbe14fb5/jtd-12-11-6966-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/0ddbff33943f/jtd-12-11-6966-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/f8d2f1db5e51/jtd-12-11-6966-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/93ea1cbaedfb/jtd-12-11-6966-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/fa63dbe14fb5/jtd-12-11-6966-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b533/7711402/0ddbff33943f/jtd-12-11-6966-f4.jpg

相似文献

[1]
Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening.

J Thorac Dis. 2020-11

[2]
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[3]
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[4]
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[6]
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[7]
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[8]
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[10]
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引用本文的文献

[1]
Patient and nodule characteristics associated with adherence to lung cancer screening in a large integrated healthcare system.

Sci Rep. 2025-8-9

[2]
Tumour-pleura relationship on computed tomography (CT) provides effective risk stratification for peripheral pulmonary nodules with Lung Imaging Reporting and Data System (Lung-RADS) score of 4X.

Quant Imaging Med Surg. 2024-10-1

[3]
Diagnostic utility of adding needle aspiration (using PeriView FLEX needle) to radial endobronchial ultrasound guide sheath transbronchial lung biopsy: a single center retrospective study.

J Thorac Dis. 2024-6-30

[4]
Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BMC Med Educ. 2024-7-9

[5]
Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

Eur Radiol. 2024-11

[6]
The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes.

Quant Imaging Med Surg. 2024-2-1

[7]
Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China.

Cancer Imaging. 2024-1-22

[8]
Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules.

Eur Radiol. 2024-5

[9]
Proposal of Modified Lung-RADS in Assessing Pulmonary Nodules of Patients with Previous Malignancies: A Primary Study.

Diagnostics (Basel). 2023-6-29

[10]
Initial low-dose computed tomography screening results and summary of participant characteristics: based on the latest Chinese guideline.

Front Oncol. 2023-5-24

本文引用的文献

[1]
Three-dimensional mean CT attenuation value of pure and part-solid ground-glass lung nodules may predict invasiveness in early adenocarcinoma.

Clin Radiol. 2019-10-18

[2]
Risk factors associated with an increase in the size of ground-glass lung nodules on chest computed tomography.

Thorac Cancer. 2019-6-2

[3]
External validation and recalibration of the Brock model to predict probability of cancer in pulmonary nodules using NLST data.

Thorax. 2019-3-21

[4]
Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

J Thorac Imaging. 2019-3

[5]
Vancouver Risk Calculator Compared with ACR Lung-RADS in Predicting Malignancy: Analysis of the National Lung Screening Trial.

Radiology. 2019-1-22

[6]
Cancer statistics, 2019.

CA Cancer J Clin. 2019-1-8

[7]
A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules.

Radiology. 2018-11-20

[8]
The decision to biopsy in a lung cancer screening program: Potential impact of risk calculators.

J Med Screen. 2018-11-12

[9]
Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules.

Ann Am Thorac Soc. 2018-10

[10]
Natural History of Persistent Pulmonary Subsolid Nodules: Long-Term Observation of Different Interval Growth.

Heart Lung Circ. 2018-9-14

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