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Management of Lung Nodules and Lung Cancer Screening During the COVID-19 Pandemic: CHEST Expert Panel Report

Mazzone, Peter J; Gould, Michael K; Arenberg, Douglas A; Chen, Alexander C; Choi, Humberto K; Detterbeck, Frank C; Farjah, Farhood; Fong, Kwun M; Iaccarino, Jonathan M; Janes, Samuel M; Kanne, Jeffrey P; Kazerooni, Ella A; MacMahon, Heber; Naidich, David P; Powell, Charles A; Raoof, Suhail; Rivera, M Patricia; Tanner, Nichole T; Tanoue, Lynn K; Tremblay, Alain; Vachani, Anil; White, Charles S; Wiener, Renda Soylemez; Silvestri, Gerard A
Background:The risks from potential exposure to coronavirus disease 2019 (COVID-19), and resource reallocation that has occurred to combat the pandemic, have altered the balance of benefits and harms that informed current (pre-COVID-19) guideline recommendations for lung cancer screening and lung nodule evaluation. Consensus statements were developed to guide clinicians managing lung cancer screening programs and patients with lung nodules during the COVID-19 pandemic. Materials and Methods:An expert panel of 24 members, including pulmonologists (n = 17), thoracic radiologists (n = 5), and thoracic surgeons (n = 2), was formed. The panel was provided with an overview of current evidence, summarized by recent guidelines related to lung cancer screening and lung nodule evaluation. The panel was convened by video teleconference to discuss and then vote on statements related to 12 common clinical scenarios. A predefined threshold of 70% of panel members voting agree or strongly agree was used to determine if there was a consensus for each statement. Items that may influence decisions were listed as notes to be considered for each scenario. Results:Twelve statements related to baseline and annual lung cancer screening (n = 2), surveillance of a previously detected lung nodule (n = 5), evaluation of intermediate and high-risk lung nodules (n = 4), and management of clinical stage I non-small cell lung cancer (n = 1) were developed and modified. All 12 statements were confirmed as consensus statements according to the voting results. The consensus statements provide guidance about situations in which it was believed to be appropriate to delay screening, defer surveillance imaging of lung nodules, and minimize nonurgent interventions during the evaluation of lung nodules and stage I non-small cell lung cancer. Conclusion:There was consensus that during the COVID-19 pandemic, it is appropriate to defer enrollment in lung cancer screening and modify the evaluation of lung nodules due to the added risks from potential exposure and the need for resource reallocation. There are multiple local, regional, and patient-related factors that should be considered when applying these statements to individual patient care.© 2020 RSNA; The American College of Chest Physicians, published by Elsevier Inc; and The American College of Radiology, published by Elsevier Inc.
PMCID:7233408
PMID: 33778716
ISSN: 2638-616x
CID: 4882362

Managing Stable Subsolid Lung Nodules: A Possible Approach [Comment]

Naidich, David P; Azour, Lea
PMID: 32186460
ISSN: 1527-1315
CID: 4352702

How I Do It: An Algorithmic Approach to the Interpretation of Diffuse Lung Disease on Chest CT Imaging

Gruden, James F; Naidich, David P; Machnicki, Stephen C; Cohen, Stuart L; Girvin, Francis; Raoof, Suhail
PMID: 31704148
ISSN: 1931-3543
CID: 4184552

Lung Hyperlucency - A Clinical-Radiologic Algorithmic Approach to Diagnosis

Cherian, Sujith V; Girvin, Francis; Naidich, David P; Machnicki, Stephen; Brown, Kevin K; Ryu, Jay H; Gupta, Nishant; Mehta, Vishisht; Estrada-Y-Martin, Rosa M; Narasimhan, Mangala; Oks, Margarita; Raoof, Suhail
Areas of diminished lung density are frequently identified both on routine chest radiographs and chest CT examinations. Colloquially referred to as "hyperlucent" foci of lung, a broad range of underlying pathophysiologic mechanisms and differential diagnoses account for these changes. Despite this, the spectrum of etiologies can be categorized into underlying parenchymal, airway and vascular related entities, respectively. The purpose of this review is to provide a practical diagnostic algorithmic approach to pulmonary hyperlucencies incorporating clinical history and characteristic imaging patterns to narrow the differential.
PMID: 31356811
ISSN: 1931-3543
CID: 4015192

Author Correction: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Ardila, Diego; Kiraly, Atilla P; Bharadwaj, Sujeeth; Choi, Bokyung; Reicher, Joshua J; Peng, Lily; Tse, Daniel; Etemadi, Mozziyar; Ye, Wenxing; Corrado, Greg; Naidich, David P; Shetty, Shravya
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
PMID: 31253948
ISSN: 1546-170x
CID: 3964032

Approach to Peribronchovascular Disease on CT

Ko, Jane P; Girvin, Francis; Moore, William; Naidich, David P
Diseases that are predominantly peribronchovascular in distribution on computed tomography by definition involve the bronchi, adjacent vasculature, and associated lymphatics involving the central or axial lung interstitium. An understanding of diseases that can present with focal peribronchovascular findings is useful for establishing diagnoses and guiding patient management. This review will cover clinical and imaging features that may assist in differentiating amongst the various causes of primarily peribronchovascular disease.
PMID: 31200868
ISSN: 1558-5034
CID: 3930282

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Ardila, Diego; Kiraly, Atilla P; Bharadwaj, Sujeeth; Choi, Bokyung; Reicher, Joshua J; Peng, Lily; Tse, Daniel; Etemadi, Mozziyar; Ye, Wenxing; Corrado, Greg; Naidich, David P; Shetty, Shravya
With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States1. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines1-6. Existing challenges include inter-grader variability and high false-positive and false-negative rates7-10. We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.
PMID: 31110349
ISSN: 1546-170x
CID: 3977392

CT screening for lung cancer: comparison of three baseline screening protocols

Henschke, Claudia I.; Yip, Rowena; Ma, Teng; Aguayo, Samuel M.; Zulueta, Javier; Yankelevitz, David F.; Henschke, Claudia I.; Yankelevitz, David F.; Yip, Rowena; Xu, Dongming; Salvatore, Mary; Flores, Raja; Wolf, Andrea; McCauley, Dorothy I.; Chen, Mildred; Libby, Daniel M.; Miettinen, Olli S.; Smith, James P.; Pasmantier, Mark; Reeves, A. P.; Markowitz, Steven; Miller, Albert; Deval, Jose Cervera; Schmidt, Heidi; Patsios, Demetris; Sone, Shusuke; Hanaoka, Takaomi; Zulueta, Javier; Montuenga, Luis; Lozano, Maria D.; Aye, Ralph; Bauer, Thomas; Canitano, Stefano; Giunta, Salvatore; Cole, Enser; Klingler, Karl; Austin, John H. M.; Pearson, Gregory D. N.; Shaham, Dorith; Aylesworth, Cheryl; Meyers, Patrick; Andaz, Shahriyour; Vafai, Davood; Naidich, David; McGuinness, Georgeann; Sheppard, Barry; Rifkin, Matthew; Thorsen, M. Kristin; Hansen, Richard; Kopel, Samuel; Mayfield, William; Luedke, Dan; Klippenstein, Donald; Litwin, Alan; Loud, Peter A.; Kohman, Leslie J.; Scalzetti, Ernest M.; Thurer, Richard; Khan, Arfa; Shah, Rakesh; Liu, Xueguo; Herzog, Gary; Yeh, Diane; Wu, Ning; Lowry, Joseph; Salvatore, Mary; Frumiento, Carmine; Mendelson, David S.; Smith, Michael V.; Korst, Robert; Taylor, Jana; Heelan, Robert T.; Ginsberg, Michelle S.; Straznicka, Michaela; Widmann, Mark; Cecchi, Gary; Matalon, Terence A. S.; Scheinberg, Paul; Odzer, Shari-Lynn; Olsen, David; Grannis, Fred; Rotter, Arnold; Ray, Daniel; Mullen, David; Wiernik, Peter H.; Cheung, Edson H.; Lim, Melissa; DeCunzo, Louis; Glassberg, Robert; Pass, Harvey; Endress, Carmen; Yoder, Mark; Shah, Palmi; Welch, Laura; Kalafer, Michael; Green, Jeremy; Camacho, Elmer; Chin, Cynthia; O\Brien, James; Gorden, David; Koch, Albert; Wiley, James
ISI:000483696900010
ISSN: 0938-7994
CID: 5323422

Is Spread of Tumor through Air Spaces a Concern for Interpreting Lung Nodules on CT Images?

Naidich, David P
PMID: 30179107
ISSN: 1527-1315
CID: 3271692

Low Dose Lung CT Screening in an Asian Population [Editorial]

Naidich, David P
PMID: 30017500
ISSN: 1878-4046
CID: 3202132