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Pura-Bryant J, Olivera JA, Antonoff MB. Biomarkers: a new frontier in lung cancer detection. Transl Lung Cancer Res 2024; 13:210-212. [PMID: 38404994 PMCID: PMC10891402 DOI: 10.21037/tlcr-23-801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 12/29/2023] [Indexed: 02/27/2024]
Affiliation(s)
| | | | - Mara B. Antonoff
- Thoracic and Cardiovascular Surgery Department, UT MD Anderson Cancer Center, Houston, TX, USA
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Stephens EKH, Guayco Sigcha J, Lopez-Loo K, Yang IA, Marshall HM, Fong KM. Biomarkers of lung cancer for screening and in never-smokers-a narrative review. Transl Lung Cancer Res 2023; 12:2129-2145. [PMID: 38025810 PMCID: PMC10654441 DOI: 10.21037/tlcr-23-291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023]
Abstract
Background and Objective Lung cancer is the leading cause of cancer-related mortality worldwide, partially attributed to late-stage diagnoses. In order to mitigate this, lung cancer screening (LCS) of high-risk patients is performed using low dose computed tomography (CT) scans, however this method is burdened by high false-positive rates and radiation exposure for patients. Further, screening programs focus on individuals with heavy smoking histories, and as such, never-smokers who may otherwise be at risk of lung cancer are often overlooked. To resolve these limitations, biomarkers have been posited as potential supplements or replacements to low-dose CT, and as such, a large body of research in this area has been produced. However, comparatively little information exists on their clinical efficacy and how this compares to current LCS strategies. Methods Here we conduct a search and narrative review of current literature surrounding biomarkers of lung cancer to supplement LCS, and biomarkers of lung cancer in never-smokers (LCINS). Key Content and Findings Many potential biomarkers of lung cancer have been identified with varying levels of sensitivity, specificity, clinical efficacy, and supporting evidence. Of the markers identified, multi-target panels of circulating microRNAs, lipids, and metabolites are likely the most clinically efficacious markers to aid current screening programs, as these provide the highest sensitivity and specificity for lung cancer detection. However, circulating lipid and metabolite levels are known to vary in numerous systemic pathologies, highlighting the need for further validation in large cohort randomised studies. Conclusions Lung cancer biomarkers is a fast-expanding area of research and numerous biomarkers with potential clinical applications have been identified. However, in all cases the level of evidence supporting clinical efficacy is not yet at a level at which it can be translated to clinical practice. The priority now should be to validate existing candidate markers in appropriate clinical contexts and work to integrating these into clinical practice.
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Affiliation(s)
- Edward K. H. Stephens
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Jazmin Guayco Sigcha
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Kenneth Lopez-Loo
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Ian A. Yang
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
| | - Henry M. Marshall
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
| | - Kwun M. Fong
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
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Toumazis I, Erdogan SA, Bastani M, Leung A, Plevritis SK. A Cost-Effectiveness Analysis of Lung Cancer Screening With Low-Dose Computed Tomography and a Diagnostic Biomarker. JNCI Cancer Spectr 2021; 5:pkab081. [PMID: 34738073 PMCID: PMC8564700 DOI: 10.1093/jncics/pkab081] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/12/2021] [Accepted: 08/20/2021] [Indexed: 12/17/2022] Open
Abstract
Background The Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) reduces the false-positive rate of lung cancer screening but introduces prolonged periods of uncertainty for indeterminate findings. We assess the cost-effectiveness of a screening program that assesses indeterminate findings earlier via a hypothetical diagnostic biomarker introduced in place of Lung-RADS 3 and 4A guidelines. Methods We evaluated the performance of the US Preventive Services Task Force (USPSTF) recommendations on lung cancer screening with and without a hypothetical noninvasive diagnostic biomarker using a validated microsimulation model. The diagnostic biomarker assesses the malignancy of indeterminate nodules, replacing Lung-RADS 3 and 4A guidelines, and is characterized by a varying sensitivity profile that depends on nodules' size, specificity, and cost. We tested the robustness of our findings through univariate sensitivity analyses. Results A lung cancer screening program per the USPSTF guidelines that incorporates a diagnostic biomarker with at least medium sensitivity profile and 90% specificity, that costs $250 or less, is cost-effective with an incremental cost-effectiveness ratio lower than $100 000 per quality-adjusted life year, and improves lung cancer-specific mortality reduction while requiring fewer screening exams than the USPSTF guidelines with Lung-RADS. A screening program with a biomarker costing $750 or more is not cost-effective. The health benefits accrued and costs associated with the screening program are sensitive to the disutility of indeterminate findings and specificity of the biomarker, respectively. Conclusions Lung cancer screening that incorporates a diagnostic biomarker, in place of Lung-RADS 3 and 4A guidelines, could improve the cost-effectiveness of the screening program and warrants further investigation.
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Affiliation(s)
- Iakovos Toumazis
- Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, CA, USA
| | - S Ayca Erdogan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Mehrad Bastani
- Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, CA, USA
| | - Ann Leung
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Sylvia K Plevritis
- Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, CA, USA
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Zhang Z, Bien J, Mori M, Jindal S, Bergan R. A way forward for cancer prevention therapy: personalized risk assessment. Oncotarget 2019; 10:6898-6912. [PMID: 31839883 PMCID: PMC6901339 DOI: 10.18632/oncotarget.27365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 11/19/2019] [Indexed: 12/17/2022] Open
Abstract
Cancer is characterized by genetic and molecular aberrations whose number and complexity increase dramatically as cells progress along the spectrum of carcinogenesis. The pharmacologic application of agents in the context of a lower burden of dysregulated cellular processes constitutes an efficient strategy to enhance therapeutic efficacy, and underlies the rationale for using cancer prevention agents in high-risk populations. A longstanding barrier to implementing this strategy is that the risk in the general population is low for any given cancer, many people would have to be treated in order to benefit a few. Therefore, identifying and treating high-risk individuals will improve the risk: benefit ratio. Currently, risk is defined by considering a relatively low number of factors. A strategy that considers multiple factors has the ability to define a much-higher-risk cohort than the general population. This article will review the rationale for evaluating multiple risk factors so as to identify individuals at highest risk. It will use breast and lung cancer as examples, will describe currently available risk assessment tools, and will discuss ongoing efforts to expand the impact of this approach. The high potential of this strategy to provide a way forward for developing cancer prevention therapy will be highlighted.
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Affiliation(s)
- Zhenzhen Zhang
- Division of Hematology/Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey Bien
- Division of Oncology, Stanford University, Palo Alto, California, USA
| | - Motomi Mori
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA.,OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, Oregon, USA
| | - Sonali Jindal
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, Oregon, USA
| | - Raymond Bergan
- Division of Hematology/Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
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Cherezov D, Hawkins SH, Goldgof DB, Hall LO, Liu Y, Li Q, Balagurunathan Y, Gillies RJ, Schabath MB. Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial. Cancer Med 2018; 7:6340-6356. [PMID: 30507033 PMCID: PMC6308046 DOI: 10.1002/cam4.1852] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/04/2018] [Accepted: 10/05/2018] [Indexed: 12/19/2022] Open
Abstract
Background Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens and performed size‐specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6‐16 mm [intermediate], and ≥16 mm [large]). Methods We extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow‐up (T1). Nodules were identified for 160 incidence cases diagnosed with lung cancer at T1 or second follow‐up screen (T2) and for 307 nodule‐positive controls that had three consecutive positive screens not diagnosed as lung cancer. The cases and controls were split into training and test cohorts; classifier models were used to identify the most predictive features. Results The final models revealed modest improvements for baseline and delta features when compared to only baseline features. The AUROCs for small‐ and intermediate‐sized nodules were 0.83 (95% CI 0.76‐0.90) and 0.76 (95% CI 0.71‐0.81) for baseline‐only radiomic features, respectively, and 0.84 (95% CI 0.77‐0.90) and 0.84 (95% CI 0.80‐0.88) for baseline and delta features, respectively. When intermediate and large nodules were combined, the AUROC for baseline‐only features was 0.80 (95% CI 0.76‐0.84) compared with 0.86 (95% CI 0.83‐0.89) for baseline and delta features. Conclusions We found modest improvements in predicting lung cancer incidence by combining baseline and delta radiomics. Radiomics could be used to improve current size‐based screening guidelines.
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Affiliation(s)
- Dmitry Cherezov
- Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida
| | - Samuel H Hawkins
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Dmitry B Goldgof
- Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida
| | - Lawrence O Hall
- Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida
| | - Ying Liu
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Qian Li
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yoganand Balagurunathan
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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