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Barta JA, Farjah F, Thomson CC, Dyer DS, Wiener RS, Slatore CG, Smith-Bindman R, Rosenthal LS, Silvestri GA, Smith RA, Gould MK. The American Cancer Society National Lung Cancer Roundtable strategic plan: Optimizing strategies for lung nodule evaluation and management. Cancer 2024. [PMID: 39347601 DOI: 10.1002/cncr.35181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Lung nodules are frequently detected on low-dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline-discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening-detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. PLAIN LANGUAGE SUMMARY: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening-detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules). Although the vast majority of lung nodules are not lung cancer, it is important to have evidence-based, standardized approaches to the evaluation and management of a lung nodule. The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms.
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Affiliation(s)
- Julie A Barta
- Division of Pulmonary and Critical Care Medicine, Jane and Leonard Korman Respiratory Institute, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Farhood Farjah
- Department of Surgery, University of Washington, Seattle, Washington, USA
| | - Carey Conley Thomson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Beth Israel Lahey Health/Mount Auburn Hospital, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Debra S Dyer
- Department of Radiology, National Jewish Health, Denver, Colorado, USA
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts, USA
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC, USA
- The Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Christopher G Slatore
- Division of Pulmonary and Critical Care Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Rebecca Smith-Bindman
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
| | - Lauren S Rosenthal
- American Cancer Society National Lung Cancer Roundtable, Atlanta, Georgia, USA
| | - Gerard A Silvestri
- Division of Pulmonary and Critical Care Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Robert A Smith
- Center for Early Cancer Detection Science, American Cancer Society, Atlanta, Georgia, USA
| | - Michael K Gould
- Department of Health Systems Science, Kaiser Permanente, Bernard J. Tyson School of Medicine, Pasadena, California, USA
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Kim RY. Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime? Cancer Biomark 2024:CBM230360. [PMID: 38427470 PMCID: PMC11300708 DOI: 10.3233/cbm-230360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.
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Prosper AE, Kammer MN, Maldonado F, Aberle DR, Hsu W. Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology 2023; 309:e222904. [PMID: 37815447 PMCID: PMC10623199 DOI: 10.1148/radiol.222904] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 10/11/2023]
Abstract
The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.
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Affiliation(s)
- Ashley Elizabeth Prosper
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Michael N. Kammer
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Fabien Maldonado
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Denise R. Aberle
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - William Hsu
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
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Farjah F, Monsell SE, Greenlee RT, Gould MK, Smith-Bindman R, Banegas MP, Schoen K, Ramaprasan A, Buist DSM. Patient and Nodule Characteristics Associated With a Lung Cancer Diagnosis Among Individuals With Incidentally Detected Lung Nodules. Chest 2023; 163:719-730. [PMID: 36191633 PMCID: PMC10154904 DOI: 10.1016/j.chest.2022.09.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 08/23/2022] [Accepted: 09/09/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Pulmonary nodules are a common incidental finding on CT imaging. Few studies have described patient and nodule characteristics associated with a lung cancer diagnosis using a population-based cohort. RESEARCH QUESTION Does a relationship exist between patient and nodule characteristics and lung cancer among individuals with incidentally detected pulmonary nodules, and can this information be used to create exploratory lung cancer prediction models with reasonable performance characteristics? STUDY DESIGN AND METHODS We conducted a retrospective cohort study of adults older than 18 years with lung nodules of any size incidentally detected by chest CT imaging between 2005 and 2015. All patients had at least 2 years of complete follow-up. To evaluate the relationship between patient and nodule characteristics and lung cancer, we used binomial regression. We used logistic regression to create prediction models, and we internally validated model performance using bootstrap optimism correction. RESULTS Among 7,240 patients with a median age of 67 years, 56% of whom were women, with a median BMI of 28 kg/m2, 56% of whom were ever smokers, 31% of whom had prior nonlung malignancy, with a median nodule size 5.6 mm, 57% of whom had multiple nodules, and 40% of whom had an upper lobe nodule, 265 patients (3.7%; 95% CI, 3.2%-4.1%) had a diagnosis of lung cancer. In a multivariate analysis, age, sex, BMI, smoking history, and nodule size and location were associated with a lung cancer diagnosis, whereas prior malignancy and nodule number and laterality were not. We were able to construct two prediction models with an area under the curve value of 0.75 (95% CI, 0.72-0.80) and reasonable calibration. INTERPRETATION Lung cancer is uncommon among individuals with incidentally detected lung nodules. Some, but not all, previously identified factors associated with lung cancer also were associated with this outcome in this sample. These findings may have implications for clinical practice, future practice guidelines, and the development of novel lung cancer prediction models for individuals with incidentally detected lung nodules.
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Affiliation(s)
- Farhood Farjah
- Department of Surgery, University of Washington, Seattle, WA.
| | - Sarah E Monsell
- Department of Biostatistics, University of Washington, Seattle, WA
| | | | - Michael K Gould
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA
| | - Rebecca Smith-Bindman
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Matthew P Banegas
- Department of Radiation Medicine and Applied Sciences, University of San Diego, San Diego, CA
| | - Kurt Schoen
- Marshfield Clinic Research Institute, Marshfield, WI
| | | | - Diana S M Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
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A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones. Noncoding RNA 2021; 7:ncrna7040080. [PMID: 34940762 PMCID: PMC8709422 DOI: 10.3390/ncrna7040080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/04/2021] [Accepted: 12/07/2021] [Indexed: 12/19/2022] Open
Abstract
The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.
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Fox AH, Tanner NT. Approaches to lung nodule risk assessment: clinician intuition versus prediction models. J Thorac Dis 2020; 12:3296-3302. [PMID: 32642253 PMCID: PMC7330782 DOI: 10.21037/jtd.2020.03.68] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Pulmonary nodules are increasingly identified on imaging exams performed for a number of clinical presentations and can pose a diagnostic problem for clinicians. Guideline-directed management algorithms are structured on nodule pre-test probability of malignancy. The risk of malignancy can be clinician-assigned or calculated utilizing validated risk prediction calculators. Once pre-test probability of cancer is estimated, nodule management options range from a conservative approach with serial imaging to more invasive measures including biopsy procedures or surgical resection. Here we review pulmonary nodule management with a focus on methods for assigning malignancy risk and highlight novel ways currently under active research to improve nodule risk assessment and management.
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Affiliation(s)
- Adam H Fox
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Nichole T Tanner
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, Charleston, SC, USA.,Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Veterans Affairs Hospital, Charleston, SC, USA
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