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Mohamed E, García Martínez DJ, Hosseini MS, Yoong SQ, Fletcher D, Hart S, Guinn BA. Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis. Carcinogenesis 2024; 45:1-22. [PMID: 38066655 DOI: 10.1093/carcin/bgad091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/27/2023] [Accepted: 12/05/2023] [Indexed: 02/13/2024] Open
Abstract
Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.
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Affiliation(s)
- Eithar Mohamed
- Centre for Biomedicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
| | - Daniel J García Martínez
- Department of Biotechnology, Pozuelo de Alarcón, University Francisco De Vitoria, Madrid, 28223, Spain
| | - Mohammad-Salar Hosseini
- Research Centre for Evidence-Based Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Si Qi Yoong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Daniel Fletcher
- Centre for Biomedicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
| | - Simon Hart
- Respiratory Medicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
| | - Barbara-Ann Guinn
- Centre for Biomedicine, Hull York Medical School, University of Hull, Kingston-upon-Hull, HU6 7RX, UK
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2
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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 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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Mang A, Zou W, Rolny V, Reck M, Cigoianu D, Schulze K, Holdenrieder S, Socinski MA, Shames DS, Wehnl B, Patil NS. Combined use of CYFRA 21-1 and CA 125 predicts survival of patients with metastatic NSCLC and stable disease in IMpower150. Tumour Biol 2024; 46:S177-S190. [PMID: 37545290 DOI: 10.3233/tub-230001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Patients with non-small cell lung cancer (NSCLC) and stable disease (SD) have an unmet clinical need to help guide early treatment adjustments. OBJECTIVE To evaluate the potential of tumor biomarkers to inform on survival outcomes in NSCLC SD patients. METHODS This post hoc analysis included 480 patients from the IMpower150 study with metastatic NSCLC, treated with chemotherapy, atezolizumab and bevacizumab combinations, who had SD at first CT scan (post-treatment initiation). Patients were stratified into high- and low-risk groups (overall survival [OS] and progression-free survival [PFS] outcomes) based on serum tumor biomarker levels. RESULTS The CYFRA 21-1 and CA 125 biomarker combination predicted OS and PFS in patients with SD. Risk of death was ~4-fold higher for the biomarker-stratified high-risk versus low-risk SD patients (hazard ratio [HR] 3.80; 95% confidence interval [CI] 3.02-4.78; p < 0.0001). OS in patients with the low- and high-risk SD was comparable to that in patients with the CT-defined partial response (PR; HR 1.10; 95% CI 0.898-1.34) and progressive disease (PD) (HR 1.05; 95% CI 0.621-1.77), respectively. The findings were similar with PFS, and consistent across treatment arms. CONCLUSIONS Biomarker testing shows potential for providing prognostic information to help direct treatment in NSCLC patients with SD. Prospective clinical studies are warranted.ClinicalTrials.gov: NCT02366143.
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Affiliation(s)
- Anika Mang
- Roche Diagnostics GmbH, Penzberg, Germany
| | - Wei Zou
- Oncology Biomarkers Development, Genentech, San Francisco, CA, USA
| | | | - Martin Reck
- Lung Clinic Grosshansdorf, Airway Research Center North, German Center of Lung Research, Grosshansdorf, Germany
| | | | - Katja Schulze
- Oncology Biomarkers Development, Genentech, San Francisco, CA, USA
| | - Stefan Holdenrieder
- Institute of Laboratory Medicine, German Heart Centre Munich, Technical University of Munich, Munich, Germany
| | | | - David S Shames
- Oncology Biomarkers Development, Genentech, San Francisco, CA, USA
| | | | - Namrata S Patil
- Oncology Biomarkers Development, Genentech, San Francisco, CA, USA
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4
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Godfrey CM, Shipe ME, Welty VF, Maiga AW, Aldrich MC, Montgomery C, Crockett J, Vaszar LT, Regis S, Isbell JM, Rickman OB, Pinkerman R, Lambright ES, Nesbitt JC, Maldonado F, Blume JD, Deppen SA, Grogan EL. The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation. Chest 2023; 164:1305-1314. [PMID: 37421973 PMCID: PMC10635839 DOI: 10.1016/j.chest.2023.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/03/2023] [Accepted: 06/01/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation. RESEARCH QUESTION Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models? STUDY DESIGN AND METHODS Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots. RESULTS Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23. INTERPRETATION The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.
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Affiliation(s)
- Caroline M Godfrey
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Maren E Shipe
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Valerie F Welty
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Amelia W Maiga
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN
| | - Melinda C Aldrich
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Jerod Crockett
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | | | - Shawn Regis
- Department of Radiation Oncology, Lahey Hospital and Medical Center, Burlington, MA
| | - James M Isbell
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Otis B Rickman
- Division of Pulmonary Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Rhonda Pinkerman
- Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN
| | - Eric S Lambright
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Jonathan C Nesbitt
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN
| | - Fabien Maldonado
- Division of Pulmonary Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jeffrey D Blume
- School of Data Science, University of Virginia, Charlottesville, VA
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN.
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5
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Rahman SJ, Chen SC, Wang YT, Gao Y, Schepmoes AA, Fillmore TL, Shi T, Chen H, Rodland KD, Massion PP, Grogan EL, Liu T. Validation of a Proteomic Signature of Lung Cancer Risk from Bronchial Specimens of Risk-Stratified Individuals. Cancers (Basel) 2023; 15:4504. [PMID: 37760474 PMCID: PMC10526486 DOI: 10.3390/cancers15184504] [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] [Received: 08/10/2023] [Revised: 09/07/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population that ultimately develops lung cancer. Previously, we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at risk for lung cancer. The purpose of this study is to validate, in an independent cohort, a selected list of 55 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM). Bronchial brushings collected from individuals at low and high risk for developing lung cancer as well as patients with lung cancer, from both a subset of the original cohort (batch 1: n = 10 per group) and an independent cohort of 149 individuals (batch 2: low risk (n = 32), high risk (n = 34), and lung cancer (n = 83)), were analyzed using multiplexed SRM assays. ALDH3A1 and AKR1B10 were found to be consistently overexpressed in the high-risk group in both batch 1 and batch 2 brushing specimens as well as in the biopsies of batch 1. Validation of highly discriminatory proteins and metabolic enzymes by SRM in a larger independent cohort supported their use to identify patients at high risk for developing lung cancer.
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Affiliation(s)
- S.M. Jamshedur Rahman
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (S.M.J.R.); (P.P.M.)
| | - Sheau-Chiann Chen
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA; (S.-C.C.); (H.C.)
| | - Yi-Ting Wang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.-T.W.); (Y.G.); (A.A.S.); (T.S.)
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.-T.W.); (Y.G.); (A.A.S.); (T.S.)
| | - Athena A. Schepmoes
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.-T.W.); (Y.G.); (A.A.S.); (T.S.)
| | - Thomas L. Fillmore
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA;
| | - Tujin Shi
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.-T.W.); (Y.G.); (A.A.S.); (T.S.)
| | - Heidi Chen
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA; (S.-C.C.); (H.C.)
| | - Karin D. Rodland
- Department of Cell, Developmental, and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, USA;
| | - Pierre P. Massion
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (S.M.J.R.); (P.P.M.)
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN 37232, USA
| | - Eric L. Grogan
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN 37232, USA
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.-T.W.); (Y.G.); (A.A.S.); (T.S.)
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6
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Khodayari Moez E, Warkentin MT, Brhane Y, Lam S, Field JK, Liu G, Zulueta JJ, Valencia K, Mesa-Guzman M, Nialet AP, Atkar-Khattra S, Davies MPA, Grant B, Murison K, Montuenga LM, Amos CI, Robbins HA, Johansson M, Hung RJ. Circulating proteome for pulmonary nodule malignancy. J Natl Cancer Inst 2023; 115:1060-1070. [PMID: 37369027 PMCID: PMC10483334 DOI: 10.1093/jnci/djad122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 05/29/2023] [Accepted: 06/22/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules. METHODS Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated. RESULTS We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001). CONCLUSIONS Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.
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Affiliation(s)
- Elham Khodayari Moez
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Matthew T Warkentin
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Yonathan Brhane
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Stephen Lam
- Integrative Oncology, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - John K Field
- Molecular & Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Geoffrey Liu
- Computational Biology and Medicine Program, Princess Margaret Cancer Center, Toronto, ON, Canada
| | - Javier J Zulueta
- Division of Pulmonary, Critical Care and Sleep Medicine, Mount Sinai Morningside Hospital, Icahn School of Medicine, New York, NY, USA
| | - Karmele Valencia
- Center of Applied Medical Research (CIMA) and Schools of Sciences and Medicine, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Centro de Investigacion Biomedica en Red de Cancer (CIBERONC), Madrid, Spain
| | - Miguel Mesa-Guzman
- Thoracic Surgery Department, Clínica Universidad de Navarra, Pamplona, Spain
| | - Andrea Pasquier Nialet
- Center of Applied Medical Research (CIMA) and Schools of Sciences and Medicine, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Centro de Investigacion Biomedica en Red de Cancer (CIBERONC), Madrid, Spain
| | | | - Michael P A Davies
- Molecular & Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Benjamin Grant
- Computational Biology and Medicine Program, Princess Margaret Cancer Center, Toronto, ON, Canada
| | - Kiera Murison
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Luis M Montuenga
- Center of Applied Medical Research (CIMA) and Schools of Sciences and Medicine, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Centro de Investigacion Biomedica en Red de Cancer (CIBERONC), Madrid, Spain
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Hilary A Robbins
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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7
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Paez R, Kammer MN, Balar A, Lakhani DA, Knight M, Rowe D, Xiao D, Heideman BE, Antic SL, Chen H, Chen SC, Peikert T, Sandler KL, Landman BA, Deppen SA, Grogan EL, Maldonado F. Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep 2023; 13:6157. [PMID: 37061539 PMCID: PMC10105767 DOI: 10.1038/s41598-023-33098-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/07/2023] [Indexed: 04/17/2023] Open
Abstract
A deep learning model (LCP CNN) for the stratification of indeterminate pulmonary nodules (IPNs) demonstrated better discrimination than commonly used clinical prediction models. However, the LCP CNN score is based on a single timepoint that ignores longitudinal information when prior imaging studies are available. Clinically, IPNs are often followed over time and temporal trends in nodule size or morphology inform management. In this study we investigated whether the change in LCP CNN scores over time was different between benign and malignant nodules. This study used a prospective-specimen collection, retrospective-blinded-evaluation (PRoBE) design. Subjects with incidentally or screening detected IPNs 6-30 mm in diameter with at least 3 consecutive CT scans prior to diagnosis (slice thickness ≤ 1.5 mm) with the same nodule present were included. Disease outcome was adjudicated by biopsy-proven malignancy, biopsy-proven benign disease and absence of growth on at least 2-year imaging follow-up. Lung nodules were analyzed using the Optellum LCP CNN model. Investigators performing image analysis were blinded to all clinical data. The LCP CNN score was determined for 48 benign and 32 malignant nodules. There was no significant difference in the initial LCP CNN score between benign and malignant nodules. Overall, the LCP CNN scores of benign nodules remained relatively stable over time while that of malignant nodules continued to increase over time. The difference in these two trends was statistically significant. We also developed a joint model that incorporates longitudinal LCP CNN scores to predict future probability of cancer. Malignant and benign nodules appear to have distinctive trends in LCP CNN score over time. This suggests that longitudinal modeling may improve radiomic prediction of lung cancer over current models. Additional studies are needed to validate these early findings.
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Affiliation(s)
- Rafael Paez
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael N Kammer
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Aneri Balar
- Department of Radiology, West Virginia University, Morgantown, WV, USA
| | - Dhairya A Lakhani
- Department of Radiology, West Virginia University, Morgantown, WV, USA
| | - Michael Knight
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dianna Rowe
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David Xiao
- Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brent E Heideman
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sanja L Antic
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Heidi Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sheau-Chiann Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tobias Peikert
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kim L Sandler
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Engineering and Computer, Vanderbilt University, Nashville, TN, USA
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fabien Maldonado
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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8
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Yu DH, Shafiq M, Batra H, Johnson M, Griscom B, Chamberlin J, Lofaro LR, Huang J, Bulman WA, Kennedy GC, Yarmus LB, Lee HJ, Feller-Kopman D. Comparing modalities for risk assessment in patients with pulmonary lesions and nondiagnostic bronchoscopy for suspected lung cancer. BMC Pulm Med 2022; 22:442. [PMID: 36434574 PMCID: PMC9700899 DOI: 10.1186/s12890-022-02181-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 09/09/2022] [Accepted: 09/28/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Bronchoscopy is commonly utilized for non-surgical sampling of indeterminant pulmonary lesions, but nondiagnostic procedures are common. Accurate assessment of the risk of malignancy is essential for decision making in these patients, yet we lack tools that perform well across this heterogeneous group of patients. We sought to evaluate the accuracy of three previously validated risk models and physician-assessed risk (PAR) in patients with a newly identified lung lesion undergoing bronchoscopy for suspected lung cancer where the result is nondiagnostic. METHODS We performed an analysis of prospective data collected for the Percepta Bronchial Genomic Classifier Multicenter Registry. PAR and three previously validated risk models (Mayo Clinic, Veteran's Affairs, and Brock) were used to determine the probability of lung cancer (low, intermediate, or high) in 375 patients with pulmonary lesions who underwent bronchoscopy for possible lung cancer with nondiagnostic pathology. Results were compared to the actual adjudicated prevalence of malignancy in each pre-test risk group, determined with a minimum of 12 months follow up after bronchoscopy. RESULTS PAR and the risk models performed poorly overall in the assessment of risk in this patient population. PAR most closely matched the observed prevalence of malignancy in patients at 12 months after bronchoscopy, but all modalities had a low area under the curve, and in all clinical models more than half of all the lesions labeled as high risk were truly or likely benign. The studied risk model calculators overestimate the risk of malignancy compared to PAR, particularly in the subset in older patients, irregularly bordered nodules, and masses > 3 cm. Overall, the risk models perform only slightly better when confined to lung nodules < 3 cm in this population. CONCLUSION The currently available tools for the assessment of risk of malignancy perform suboptimally in patients with nondiagnostic findings following a bronchoscopic evaluation for lung cancer. More accurate and objective tools for risk assessment are needed. TRIAL REGISTRATION not applicable.
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Affiliation(s)
- Diana H. Yu
- grid.266102.10000 0001 2297 6811Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, University of California, San Francisco, San Francisco, USA CA ,grid.413077.60000 0004 0434 9023UCSF Medical Center, 505 Parnassus Ave, 9414 San Francisco, CA USA
| | - Majid Shafiq
- grid.62560.370000 0004 0378 8294Brigham and Women’s Hospital, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Boston, MA USA
| | - Hitesh Batra
- grid.265892.20000000106344187Department of Medicine, Division of Pulmonary and Critical Care Medicine Birmingham, University of Alabama at Birmingham, Birmingham, AL USA
| | - Marla Johnson
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Bailey Griscom
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Janna Chamberlin
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Lori R. Lofaro
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Jing Huang
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - William A. Bulman
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Giulia C. Kennedy
- grid.503590.a0000 0004 5345 9448Veracyte, Inc., South San Francisco, CA USA
| | - Lonny B. Yarmus
- grid.21107.350000 0001 2171 9311Division of Pulmonary and Critical Care Medicine, Section of Interventional Pulmonology, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Hans J. Lee
- grid.21107.350000 0001 2171 9311Division of Pulmonary and Critical Care Medicine, Section of Interventional Pulmonology, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - David Feller-Kopman
- grid.254880.30000 0001 2179 2404Department of Medicine, Division of Pulmonary and Critical Care Medicine, Dartmouth College, Hanover, NH USA
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9
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Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, Cohen A, Simoff MJ, Massion PP, Filippini C, Gleeson FV, Vachani A. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology 2022; 304:683-691. [PMID: 35608444 PMCID: PMC9434821 DOI: 10.1148/radiol.212182] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 03/02/2022] [Accepted: 03/16/2022] [Indexed: 12/25/2022]
Abstract
Background Limited data are available regarding whether computer-aided diagnosis (CAD) improves assessment of malignancy risk in indeterminate pulmonary nodules (IPNs). Purpose To evaluate the effect of an artificial intelligence-based CAD tool on clinician IPN diagnostic performance and agreement for both malignancy risk categories and management recommendations. Materials and Methods This was a retrospective multireader multicase study performed in June and July 2020 on chest CT studies of IPNs. Readers used only CT imaging data and provided an estimate of malignancy risk and a management recommendation for each case without and with CAD. The effect of CAD on average reader diagnostic performance was assessed using the Obuchowski-Rockette and Dorfman-Berbaum-Metz method to calculate estimates of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Multirater Fleiss κ statistics were used to measure interobserver agreement for malignancy risk and management recommendations. Results A total of 300 chest CT scans of IPNs with maximal diameters of 5-30 mm (50.0% malignant) were reviewed by 12 readers (six radiologists, six pulmonologists) (patient median age, 65 years; IQR, 59-71 years; 164 [55%] men). Readers' average AUC improved from 0.82 to 0.89 with CAD (P < .001). At malignancy risk thresholds of 5% and 65%, use of CAD improved average sensitivity from 94.1% to 97.9% (P = .01) and from 52.6% to 63.1% (P < .001), respectively. Average reader specificity improved from 37.4% to 42.3% (P = .03) and from 87.3% to 89.9% (P = .05), respectively. Reader interobserver agreement improved with CAD for both the less than 5% (Fleiss κ, 0.50 vs 0.71; P < .001) and more than 65% (Fleiss κ, 0.54 vs 0.71; P < .001) malignancy risk categories. Overall reader interobserver agreement for management recommendation categories (no action, CT surveillance, diagnostic procedure) also improved with CAD (Fleiss κ, 0.44 vs 0.52; P = .001). Conclusion Use of computer-aided diagnosis improved estimation of indeterminate pulmonary nodule malignancy risk on chest CT scans and improved interobserver agreement for both risk stratification and management recommendations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.
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Affiliation(s)
- Roger Y. Kim
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Jason L. Oke
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Lyndsey C. Pickup
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Reginald F. Munden
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Travis L. Dotson
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Christina R. Bellinger
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Avi Cohen
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Michael J. Simoff
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Pierre P. Massion
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Claire Filippini
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Fergus V. Gleeson
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
| | - Anil Vachani
- From the Division of Pulmonary, Allergy, and Critical Care,
Department of Medicine, Perelman School of Medicine, University of Pennsylvania,
Suite 216, Stemmler Hall, 3450 Hamilton Walk, Philadelphia, PA 19104 (R.Y.K.,
A.V.); Nuffield Department of Primary Care Health Sciences, University of
Oxford, Oxford, United Kingdom (J.L.O.); Optellum, Oxford, United Kingdom
(L.C.P.); Department of Radiology and Radiological Science, Medical University
of South Carolina, Charleston, SC (R.F.M.); Department of Pulmonary, Critical
Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine,
Winston-Salem, NC (T.L.D., C.R.B.); Division of Pulmonary and Critical Care
Medicine, Department of Medicine, Henry Ford Health System, Detroit, Mich (A.C.,
M.J.S.); Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt
Ingram Cancer Center, Nashville, Tenn (P.P.M.); and Department of Oncology,
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (C.F.,
F.V.G.)
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10
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Assessment of Specific Tumoral Markers, Inflammatory Status, and Vitamin D Metabolism before and after the First Chemotherapy Cycle in Patients with Lung Cancer. BIOLOGY 2022; 11:biology11071033. [PMID: 36101414 PMCID: PMC9312139 DOI: 10.3390/biology11071033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/27/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022]
Abstract
Background: We aimed to investigate the changes of inflammatory status reflected by serum levels of chitotriosidase (CHT) and neopterin, and how specific tumor markers such as neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCCA), as well as vitamin D metabolism assessed by vitamin D receptor (VDR) and 25-hydroxy vitamin D3 (25OHD3), were modified after the first cycle of chemotherapy in patients with lung cancer. Methods: We performed this first pilot study on twenty patients diagnosed with lung cancer by investigating the serum concentrations of CHT, neopterin, NSE, SCCA, VDR and 25OHD3 before and after the first cycle of chemotherapy. Results: The post-treatment values of NSE were significantly lower compared to the pre-treatment levels (14.37 vs. 17.10 ng/mL, p = 0.031). We noticed a similar trend in neopterin levels, but the difference was only marginally significant (1.44 vs. 1.17 ng/mL, p = 0.069). On the contrary, the variations of circulating SCCA, CHT, neopterin, VDR and 25OHD3, before and after treatment, did not reach statistical significance. Conclusion: Only circulating NSE was treatment responsive to the first chemotherapy cycle in patients with lung cancer, while inflammatory markers and vitamin D status were not significantly modified.
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11
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Kammer MN, Deppen SA, Antic S, Jamshedur Rahman S, Eisenberg R, Maldonado F, Aldrich MC, Sandler KL, Landman B, Massion PP, Grogan EL. The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies. Cancer Biomark 2022; 33:449-465. [DOI: 10.3233/cbm-210382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.
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Affiliation(s)
- Michael N. Kammer
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephen A. Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN, USA
| | - Sanja Antic
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - S.M. Jamshedur Rahman
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rosana Eisenberg
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fabien Maldonado
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Melinda C. Aldrich
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kim L. Sandler
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Pierre P. Massion
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN, USA
- Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric L. Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN, USA
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12
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Li J, Zhao J, Lei Y, Chen Y, Cheng M, Wei X, Liu J, Liu P, Chen R, Yin X, Shang L, Li X. Coronary Atherosclerotic Disease and Cancer: Risk Factors and Interrelation. Front Cardiovasc Med 2022; 9:821267. [PMID: 35463783 PMCID: PMC9021452 DOI: 10.3389/fcvm.2022.821267] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/15/2022] [Indexed: 01/06/2023] Open
Abstract
BackgroundIn our clinical work, we found that cancer patients were susceptible to coronary atherosclerotic heart disease (CAD). However, less is known about the relationship between CAD and cancer. The present study aimed to identify the risk factors for CAD and cancer, as well as the relationship between CAD and cancer.MethodsIn this retrospective study, 1600 patients between January 2012 and June 2019 were enrolled and divided into groups according to whether they had CAD or cancer. Single-factor and multivariate analysis methods were applied to examine the risk factors for CAD and cancer.Results(1) Cancer prevalence was significantly higher in patients with CAD than in patients without CAD (47.2 vs. 20.9%). The prevalence of CAD in cancer and non-cancer patients was 78.9 and 52.4%, respectively. (2) Multivariable logistic regression showed that patients with cancer had a higher risk of developing CAD than non-cancer patients (OR: 2.024, 95% CI: 1.475 to 2.778, p < 0.001). Respiratory (OR: 1.981, 95% CI: 1.236–3.175, p = 0.005), digestive (OR: 1.899, 95% CI: 1.177–3.064, p = 0.009) and urogenital (OR: 3.595, 95% CI: 1.696–7.620, p = 0.001) cancers were significantly associated with a higher risk of CAD compared with no cancer. (3) Patients with CAD also had a higher risk of developing cancer than non-CAD patients (OR = 2.157, 95% CI: 1.603 to 2.902, p < 0.001). Patients in the Alanine aminotransferase (ALT) level ≥ 40 U/L group had a lower risk of cancer than patients in the ALT level < 20 U/L group (OR: 0.490, 95% CI: 0.333–0.722, p < 0.001). (4) An integrated variable (Y = 0.205 × 10–1 age − 0.595 × 10–2 HGB − 0.116 × 10–1 ALT + 0.135 FIB) was identified for monitoring the occurrence of cancer among CAD patients, with an AUC of 0.720 and clinical sensitivity/specificity of 0.617/0.711.Conclusion(1) We discovered that CAD was an independent risk factor for cancer and vice versa. (2) Digestive, respiratory and urogenital cancers were independent risk factors for CAD. (3) We created a formula for the prediction of cancer among CAD patients. (4) ALT, usually considered a risk factor, was proven to be a protective factor for cancer in this study.
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Affiliation(s)
- Jinjing Li
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Jieqiong Zhao
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Yonghong Lei
- Department of Plastic Surgery, Chinese PLA General Hospital, Beijing, China
| | - Yan Chen
- Department of Cardiology, People’s Hospital of Taishan, Taishan, China
| | - Miaomiao Cheng
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Xiaoqing Wei
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Jing Liu
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Pengyun Liu
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Ruirui Chen
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Xiaoqing Yin
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
| | - Lei Shang
- Department of Health Statistics, School of Public Health, The Fourth Military Medical University, Xi’an, China
- Lei Shang,
| | - Xue Li
- Department of Cardiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Xue Li,
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Arenberg D. Integrated Biomarkers for Pulmonary Nodules: Proving What Is Possible. Am J Respir Crit Care Med 2021; 204:1247-1248. [PMID: 34582716 PMCID: PMC8786076 DOI: 10.1164/rccm.202108-2002ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Douglas Arenberg
- Department of Internal Medicine University of Michigan Medical School Ann Arbor, Michigan
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14
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Extracellular Vesicles: New Tools for Early Diagnosis of Breast and Genitourinary Cancers. Int J Mol Sci 2021; 22:ijms22168430. [PMID: 34445131 PMCID: PMC8395117 DOI: 10.3390/ijms22168430] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/29/2021] [Accepted: 08/03/2021] [Indexed: 12/12/2022] Open
Abstract
Breast cancers and cancers of the genitourinary tract are the most common malignancies among men and women and are still characterized by high mortality rates. In order to improve the outcomes, early diagnosis is crucial, ideally by applying non-invasive and specific biomarkers. A key role in this field is played by extracellular vesicles (EVs), lipid bilayer-delimited structures shed from the surface of almost all cell types, including cancer cells. Subcellular structures contained in EVs such as nucleic acids, proteins, and lipids can be isolated and exploited as biomarkers, since they directly stem from parental cells. Furthermore, it is becoming even more evident that different body fluids can also serve as sources of EVs for diagnostic purposes. In this review, EV isolation and characterization methods are described. Moreover, the potential contribution of EV cargo for diagnostic discovery purposes is described for each tumor.
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Abstract
PURPOSE OF REVIEW Lung cancer remains the leading cause of cancer-related death in the United States, with poor overall 5-year survival. Early detection and diagnosis are key to survival as demonstrated in lung cancer screening trials. However, with increasing implementation of screening guidelines and use of computed tomography, there has been a sharp rise in the incidence of indeterminate pulmonary nodules (IPNs). Risk stratification of IPNs, particularly those in the intermediate-risk category, remains challenging in clinical practice. Individual risk factors, imaging characteristics, biomarkers, and prediction models are currently used to assist in risk stratifying patients, but such strategies remain suboptimal. This review focuses on established risk stratification methods, current areas of research, and future directions. RECENT FINDINGS The multitude of yearly incidental and screening-detected IPNs, its management-related healthcare costs, and risk of invasive procedures provides a strong rationale for risk stratification efforts. The development of new molecular and imaging biomarkers to discriminate benign from malignant lung nodules shows great promise. Yet, risk stratification methods need integration into the diagnostic workflow and await validation in prospective, biomarker-driven clinical trials. SUMMARY Novel biomarkers and new imaging analysis, including radiomics and deep-learning methods, have been developed to optimize the risk stratification of IPNs. While promising, additional validation and clinical studies are needed before they can be part of routine clinical practice.
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Affiliation(s)
- Rafael Paez
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center
| | - Michael N Kammer
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center
- Department of Chemistry, Vanderbilt University
| | - Pierre Massion
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center
- Cancer Early Detection and Prevention Initiative, Vanderbilt-Ingram Cancer Center
- Pulmonary and Critical Care Section, Medical Service, Tennessee Valley Healthcare System, Nashville Campus, Nashville, Tennessee, USA
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16
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Up-regulation of oxysterol-binding protein 3 in lung tissue of patients with non-small lung cancer. GENE REPORTS 2021. [DOI: 10.1016/j.genrep.2020.100998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Choe W, Chae JD, Lee BH, Kim SH, Park SY, Nimse SB, Kim J, Warkad SD, Song KS, Oh AC, Hong YJ, Kim T. 9G Test TM Cancer/Lung: A Desirable Companion to LDCT for Lung Cancer Screening. Cancers (Basel) 2020; 12:cancers12113192. [PMID: 33143045 PMCID: PMC7692999 DOI: 10.3390/cancers12113192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/26/2020] [Accepted: 10/29/2020] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Lung cancer is the most common cause of cancer-related deaths globally. Patients diagnosed at early-stage (0–I) have a higher survival rate than the metastasized stages (III–IV). Thus, there is great potential to reduce mortality by diagnosing lung cancer at stage 0~I through community screening. LDCT is a promising method, but it has a high false-positive rate. Therefore, a biomarker test that can be used in combination with LDCT for lung cancer screening to reduce false-positive rates is highly awaited. The present study evaluated the applicability of 9G testTM Cancer/Lung test to detect stage 0~IV lung cancer. 9G testTM Cancer/Lung test detects stage I, stage II, stage III, and stage IV cancers with the sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3%. These results indicate that the 9G testTM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Abstract A complimentary biomarker test that can be used in combination with LDCT for lung cancer screening is highly desirable to improve the diagnostic capacity of LDCT and reduce the false-positive rates. Most importantly, the stage I lung cancer detection rate can be dramatically increased by the simultaneous use of a biomarker test with LDCT. The present study was conducted to evaluate 9G testTM Cancer/Lung’s sensitivity and specificity in detecting Stage 0~IV lung cancer. The obtained results indicate that the 9G testTM Cancer/Lung can detect lung cancer with overall sensitivity and specificity of 75.0% (69.1~80.3) and 97.3% (95.0~98.8), respectively. The detection of stage I, stage II, stage III, and stage IV cancers with sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3% have never been reported before. The receiver operating characteristic curve analysis allowed us to determine the population-weighted AUC of 0.93 (95% CI, 0.91–0.95). These results indicate that the 9G testTM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Furthermore, obtained results indicate that the use of 9G testTM Cancer/Lung with LDCT for lung cancer screening can increase stage I cancer detection, which is crucial to improve the currently low 5-year survival rates.
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Affiliation(s)
- Wonho Choe
- Nowon Eulji Medical Center, Department of Laboratory Medicine, Eulji University, Seoul 01830, Korea
| | - Jeong Don Chae
- Nowon Eulji Medical Center, Department of Laboratory Medicine, Eulji University, Seoul 01830, Korea
| | - Byoung-Hoon Lee
- Nowon Eulji Medical Center, Department of Pulmonology and Allergy, Eulji University, Seoul 01830, Korea
| | - Sang-Hoon Kim
- Nowon Eulji Medical Center, Department of Pulmonology and Allergy, Eulji University, Seoul 01830, Korea
| | - So Young Park
- Nowon Eulji Medical Center, Department of Pulmonology and Allergy, Eulji University, Seoul 01830, Korea
| | - Satish Balasaheb Nimse
- Institute of Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 24252, Korea
| | - Junghoon Kim
- Institute of Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 24252, Korea
| | | | - Keum-Soo Song
- Biometrix Technology, Inc. 2-2 Bio Venture Plaza 56, Chuncheon 24232, Korea
| | - Ae-Chin Oh
- Departments of Laboratory Medicine, Korea Cancer Center Hospital, Seoul 01812, Korea
| | - Young Jun Hong
- Departments of Laboratory Medicine, Korea Cancer Center Hospital, Seoul 01812, Korea
| | - Taisun Kim
- Institute of Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 24252, Korea
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