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Kohaar I, Hodges N, Srivastava S. Biomarkers in Cancer Screening: Promises and Challenges in Cancer Early Detection. Hematol Oncol Clin North Am 2024:S0889-8588(24)00046-7. [PMID: 38782647 DOI: 10.1016/j.hoc.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Cancer continues to be one the leading causes of death worldwide, primarily due to the late detection of the disease. Cancers detected at early stages may enable more effective intervention of the disease. However, most cancers lack well-established screening procedures except for cancers with an established early asymptomatic phase and clinically validated screening tests. There is a critical need to identify and develop assays/tools in conjunction with imaging approaches for precise screening and detection of the aggressive disease at an early stage. New developments in molecular cancer screening and early detection include germline testing, synthetic biomarkers, and liquid biopsy approaches.
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Affiliation(s)
- Indu Kohaar
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, NIH, 9609 Medical Center Drive, NCI Shady Grove Building, Rockville, MD 20850, USA
| | - Nicholos Hodges
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, NIH, 9609 Medical Center Drive, NCI Shady Grove Building, Rockville, MD 20850, USA
| | - Sudhir Srivastava
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, NIH, 9609 Medical Center Drive, NCI Shady Grove Building, Rockville, MD 20850, USA.
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2
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Cardillo G, Petersen RH, Ricciardi S, Patel A, Lodhia JV, Gooseman MR, Brunelli A, Dunning J, Fang W, Gossot D, Licht PB, Lim E, Roessner ED, Scarci M, Milojevic M. European guidelines for the surgical management of pure ground-glass opacities and part-solid nodules: Task Force of the European Association of Cardio-Thoracic Surgery and the European Society of Thoracic Surgeons. Eur J Cardiothorac Surg 2023; 64:ezad222. [PMID: 37243746 DOI: 10.1093/ejcts/ezad222] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/10/2023] [Accepted: 05/26/2023] [Indexed: 05/29/2023] Open
Affiliation(s)
- Giuseppe Cardillo
- Unit of Thoracic Surgery, Azienda Ospedaliera San Camillo Forlanini, Rome, Italy
- Unicamillus-Saint Camillus University of Health Sciences, Rome, Italy
| | - René Horsleben Petersen
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Sara Ricciardi
- Unit of Thoracic Surgery, Azienda Ospedaliera San Camillo Forlanini, Rome, Italy
- Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Akshay Patel
- Department of Thoracic Surgery, University Hospitals Birmingham, England, United Kingdom
- Institute of Immunology and Immunotherapy, University of Birmingham, United Kingdom
| | - Joshil V Lodhia
- Department of Thoracic Surgery, St James University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Michael R Gooseman
- Department of Thoracic Surgery, Hull University Teaching Hospitals NHS Trust, and Hull York Medical School, University of Hull, Hull, United Kingdom
| | - Alessandro Brunelli
- Department of Thoracic Surgery, St James University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Joel Dunning
- James Cook University Hospital Middlesbrough, United Kingdom
| | - Wentao Fang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Jiaotong University Medical School, Shangai, China
| | - Dominique Gossot
- Department of Thoracic Surgery, Curie-Montsouris Thoracic Institute, Paris, France
| | - Peter B Licht
- Department of Cardiothoracic Surgery, Odense University Hospital, Odense, Denmark
| | - Eric Lim
- Academic Division of Thoracic Surgery, The Royal Brompton Hospital and Imperial College London, United Kingdom
| | - Eric Dominic Roessner
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Marco Scarci
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, Hammersmith Hospital, London, United Kingdom
| | - Milan Milojevic
- Department of Cardiac Surgery and Cardiovascular Research, Dedinje Cardiovascular Institute, Belgrade, Serbia
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands
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3
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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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4
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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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5
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Voss T, Krag M, Martiny F, Heleno B, Jørgensen KJ, Brandt Brodersen J. Quantification of overdiagnosis in randomised trials of cancer screening: an overview and re-analysis of systematic reviews. Cancer Epidemiol 2023; 84:102352. [PMID: 36963292 DOI: 10.1016/j.canep.2023.102352] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/26/2023] [Accepted: 03/06/2023] [Indexed: 03/26/2023]
Abstract
The degree of overdiagnosis in common cancer screening trials is uncertain due to inadequate design of trials, varying definition and methods used to estimate overdiagnosis. Therefore, we aimed to quantify the risk of overdiagnosis for the most widely implemented cancer screening programmes and assess the implications of design limitations and biases in cancer screening trials on the estimates of overdiagnosis by conducting an overview and re-analysis of systematic reviews of cancer screening. We searched PubMed and the Cochrane Library from their inception dates to November 29, 2021. Eligible studies included systematic reviews of randomised trials comparing cancer screening interventions to no screening, which reported cancer incidence for both trial arms. We extracted data on study characteristics, cancer incidence and assessed the risk of bias using the Cochrane Collaboration's risk of bias tool. We included 19 trials described in 30 articles for review, reporting results for the following types of screening: mammography for breast cancer, chest X-ray or low-dose CT for lung cancer, alpha-foetoprotein and ultrasound for liver cancer, digital rectal examination, prostate-specific antigen, and transrectal ultrasound for prostate cancer, and CA-125 test and/or ultrasound for ovarian cancer. No trials on screening for melanoma were eligible. Only one trial (5%) had low risk in all bias domains, leading to a post-hoc meta-analysis, excluding trials with high risk of bias in critical domains, finding the extent of overdiagnosis ranged from 17% to 38% across cancer screening programmes. We conclude that there is a significant risk of overdiagnosis in the included randomised trials on cancer screening. We found that trials were generally not designed to estimate overdiagnosis and many trials had high risk of biases that may draw the estimates of overdiagnosis towards the null. In effect, the true extent of overdiagnosis due to cancer screening is likely underestimated.
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Affiliation(s)
- Theis Voss
- The Centre of General Practice in Copenhagen, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Post box 2099, DK-1014 Copenhagen K, Denmark.
| | - Mikela Krag
- The Centre of General Practice in Copenhagen, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Post box 2099, DK-1014 Copenhagen K, Denmark
| | - Frederik Martiny
- The Centre of General Practice in Copenhagen, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Post box 2099, DK-1014 Copenhagen K, Denmark; Center for Social Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Bruno Heleno
- CHRC, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM , Universidade Nova de Lisboa, Lisbon, Portugal
| | - Karsten Juhl Jørgensen
- Centre for Evidence-Based Medicine Odense (CEBMO) and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, JB Winsløwsvej 9b, 3rd Floor, 5000 Odense, Denmark; Open Patient data Exploratory Network (OPEN), Odense University Hospital, Odense, Denmark
| | - John Brandt Brodersen
- The Centre of General Practice in Copenhagen, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Post box 2099, DK-1014 Copenhagen K, Denmark; The Research Unit for General Practice in Region Zealand, Øster Farimagsgade 5, Post box 2099, DK-1014 Copenhagen K, Denmark; Research Unit for General Practice, Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø
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6
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Contextualizing the Role of Volumetric Analysis in Pulmonary Nodule Assessment: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2023; 220:314-329. [PMID: 36129224 DOI: 10.2214/ajr.22.27830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Pulmonary nodules are managed on the basis of their size and morphologic characteristics. Radiologists are familiar with assessing nodule size by measuring diameter using manually deployed electronic calipers. Size may also be assessed with 3D volumetric measurements (referred to as volumetry) obtained with software. Nodule size and growth are more accurately assessed with volumetry than on the basis of diameter, and the evidence supporting clinical use of volumetry has expanded, driven by its use in lung cancer screening nodule management algorithms in Europe. The application of volumetry has the potential to reduce recommendations for imaging follow-up of indeterminate solid nodules without impacting cancer detection. Although changes in scanning conditions and volumetry software packages can lead to variation in volumetry results, ongoing technical advances have improved the reliability of calculated volumes. Volumetry is now the primary method for determining size of solid nodules in the European lung cancer screening position statement and British Thoracic Society recommendations. The purposes of this article are to review technical aspects, advantages, and limitations of volumetry and, by considering specific scenarios, to contextualize the use of volumetry with respect to its importance in morphologic evaluation, its role in predicting malignancy in risk models, and its practical impact on nodule management. Implementation challenges and areas requiring further evidence are also highlighted.
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7
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Bonney A, Malouf R, Marchal C, Manners D, Fong KM, Marshall HM, Irving LB, Manser R. Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality. Cochrane Database Syst Rev 2022; 8:CD013829. [PMID: 35921047 PMCID: PMC9347663 DOI: 10.1002/14651858.cd013829.pub2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population level. A previous Cochrane Review found limited evidence for the effectiveness of lung cancer screening with chest radiography (CXR) or sputum cytology in reducing lung cancer-related mortality, however there has been increasing evidence supporting screening with low-dose computed tomography (LDCT). OBJECTIVES: To determine whether screening for lung cancer using LDCT of the chest reduces lung cancer-related mortality and to evaluate the possible harms of LDCT screening. SEARCH METHODS We performed the search in collaboration with the Information Specialist of the Cochrane Lung Cancer Group and included the Cochrane Lung Cancer Group Trial Register, Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, current issue), MEDLINE (accessed via PubMed) and Embase in our search. We also searched the clinical trial registries to identify unpublished and ongoing trials. We did not impose any restriction on language of publication. The search was performed up to 31 July 2021. SELECTION CRITERIA: Randomised controlled trials (RCTs) of lung cancer screening using LDCT and reporting mortality or harm outcomes. DATA COLLECTION AND ANALYSIS: Two review authors were involved in independently assessing trials for eligibility, extraction of trial data and characteristics, and assessing risk of bias of the included trials using the Cochrane RoB 1 tool. We assessed the certainty of evidence using GRADE. Primary outcomes were lung cancer-related mortality and harms of screening. We performed a meta-analysis, where appropriate, for all outcomes using a random-effects model. We only included trials in the analysis of mortality outcomes if they had at least 5 years of follow-up. We reported risk ratios (RRs) and hazard ratios (HRs), with 95% confidence intervals (CIs) and used the I2 statistic to investigate heterogeneity. MAIN RESULTS: We included 11 trials in this review with a total of 94,445 participants. Trials were conducted in Europe and the USA in people aged 40 years or older, with most trials having an entry requirement of ≥ 20 pack-year smoking history (e.g. 1 pack of cigarettes/day for 20 years or 2 packs/day for 10 years etc.). One trial included male participants only. Eight trials were phase three RCTs, with two feasibility RCTs and one pilot RCT. Seven of the included trials had no screening as a comparison, and four trials had CXR screening as a comparator. Screening frequency included annual, biennial and incrementing intervals. The duration of screening ranged from 1 year to 10 years. Mortality follow-up was from 5 years to approximately 12 years. None of the included trials were at low risk of bias across all domains. The certainty of evidence was moderate to low across different outcomes, as assessed by GRADE. In the meta-analysis of trials assessing lung cancer-related mortality, we included eight trials (91,122 participants), and there was a reduction in mortality of 21% with LDCT screening compared to control groups of no screening or CXR screening (RR 0.79, 95% CI 0.72 to 0.87; 8 trials, 91,122 participants; moderate-certainty evidence). There were probably no differences in subgroups for analyses by control type, sex, geographical region, and nodule management algorithm. Females appeared to have a larger lung cancer-related mortality benefit compared to males with LDCT screening. There was also a reduction in all-cause mortality (including lung cancer-related) of 5% (RR 0.95, 95% CI 0.91 to 0.99; 8 trials, 91,107 participants; moderate-certainty evidence). Invasive tests occurred more frequently in the LDCT group (RR 2.60, 95% CI 2.41 to 2.80; 3 trials, 60,003 participants; moderate-certainty evidence). However, analysis of 60-day postoperative mortality was not significant between groups (RR 0.68, 95% CI 0.24 to 1.94; 2 trials, 409 participants; moderate-certainty evidence). False-positive results and recall rates were higher with LDCT screening compared to screening with CXR, however there was low-certainty evidence in the meta-analyses due to heterogeneity and risk of bias concerns. Estimated overdiagnosis with LDCT screening was 18%, however the 95% CI was 0 to 36% (risk difference (RD) 0.18, 95% CI -0.00 to 0.36; 5 trials, 28,656 participants; low-certainty evidence). Four trials compared different aspects of health-related quality of life (HRQoL) using various measures. Anxiety was pooled from three trials, with participants in LDCT screening reporting lower anxiety scores than in the control group (standardised mean difference (SMD) -0.43, 95% CI -0.59 to -0.27; 3 trials, 8153 participants; low-certainty evidence). There were insufficient data to comment on the impact of LDCT screening on smoking behaviour. AUTHORS' CONCLUSIONS: The current evidence supports a reduction in lung cancer-related mortality with the use of LDCT for lung cancer screening in high-risk populations (those over the age of 40 with a significant smoking exposure). However, there are limited data on harms and further trials are required to determine participant selection and optimal frequency and duration of screening, with potential for significant overdiagnosis of lung cancer. Trials are ongoing for lung cancer screening in non-smokers.
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Affiliation(s)
- Asha Bonney
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
- Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Reem Malouf
- National Perinatal Epidemiology Unit (NPEU), University of Oxford, Oxford, UK
| | | | - David Manners
- Respiratory Medicine, Midland St John of God Public and Private Hospital, Midland, Australia
| | - Kwun M Fong
- Thoracic Medicine Program, The Prince Charles Hospital, Brisbane, Australia
- UQ Thoracic Research Centre, School of Medicine, The University of Queensland, Brisbane, Australia
| | - Henry M Marshall
- School of Medicine, The University of Queensland, Brisbane, Australia
| | - Louis B Irving
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
| | - Renée Manser
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
- Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
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8
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Wang X, Gao M, Xie J, Deng Y, Tu W, Yang H, Liang S, Xu P, Zhang M, Lu Y, Fu C, Li Q, Fan L, Liu S. Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol 2022; 12:892890. [PMID: 35747810 PMCID: PMC9209648 DOI: 10.3389/fonc.2022.892890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention. Methods Our study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’. Results The image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset. Conclusion The deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.
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Affiliation(s)
- Xiang Wang
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Man Gao
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Jicai Xie
- Department of Radiology, The Second People’s Hospital of Yuhuan, Yuhuan, China
| | - Yanfang Deng
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Hua Yang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Shuang Liang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Panlong Xu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Mingzi Zhang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Yang Lu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - ChiCheng Fu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Qiong Li
- Department of Radiology, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
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9
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Chen Y, Han Q, Huang Z, Lyu M, Ai Z, Liang Y, Yan H, Wang M, Xiang Z. Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis. Front Surg 2022; 9:817443. [PMID: 36017515 PMCID: PMC9396547 DOI: 10.3389/fsurg.2022.817443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/09/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses. Methods Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis. Results A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D* (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in “nodules or masses” than that in “nodules.” Conclusions The parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses. Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664
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Affiliation(s)
- Yirong Chen
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Zhiwei Huang
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Mo Lyu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
- School of Life Sciences, South China Normal University, Guangzhou, China
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yuying Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Haowen Yan
- Department of Oncology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Mengzhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, China
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
- Correspondence: Zhiming Xiang
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10
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Lung Cancer Screening in Asbestos-Exposed Populations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052688. [PMID: 35270380 PMCID: PMC8910511 DOI: 10.3390/ijerph19052688] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 12/19/2022]
Abstract
Asbestos exposure is the most important cause of occupational lung cancer mortality. Two large randomized clinical trials in the U.S. and Europe conclusively demonstrate that annual low-dose chest CT (LDCT) scan screening reduces lung cancer mortality. Age and smoking are the chief risk factors tested in LDCT studies, but numerous risk prediction models that incorporate additional lung cancer risk factors have shown excellent performance. The studies of LDCT in asbestos-exposed populations shows favorable results but are variable in design and limited in size and generalizability. Outstanding questions include how to: (1) identify workers appropriate for screening, (2) organize screening programs, (3) inform and motivate people to screen, and (4) incorporate asbestos exposure into LDCT decision-making in clinical practice. Conclusion: Screening workers aged ≥50 years with a history of ≥5 years asbestos exposure (or fewer years given intense exposure) in combination with either (a) a history of smoking at least 10 pack-years with no limit on time since quitting, or (b) a history of asbestos-related fibrosis, chronic lung disease, family history of lung cancer, personal history of cancer, or exposure to multiple workplace lung carcinogens is a reasonable approach to LDCT eligibility, given current knowledge. The promotion of LDCT-based screening among asbestos-exposed workers is an urgent priority.
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11
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Wu Z, Wang F, Cao W, Qin C, Dong X, Yang Z, Zheng Y, Luo Z, Zhao L, Yu Y, Xu Y, Li J, Tang W, Shen S, Wu N, Tan F, Li N, He J. Lung cancer risk prediction models based on pulmonary nodules: A systematic review. Thorac Cancer 2022; 13:664-677. [PMID: 35137543 PMCID: PMC8888150 DOI: 10.1111/1759-7714.14333] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 10/25/2022] Open
Abstract
BACKGROUND Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models. METHODS The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed. RESULTS A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets. CONCLUSION The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.
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Affiliation(s)
- Zheng Wu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Cao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Qin
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuesi Dong
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuoyu Yang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yadi Zheng
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zilin Luo
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liang Zhao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yiwen Yu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongjie Xu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Tang
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sipeng Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Ning Wu
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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12
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Zhang K, Wei Z, Nie Y, Shen H, Wang X, Wang J, Yang F, Chen K. Comprehensive analysis of clinical logistic and machine learning based models for the evaluation of pulmonary nodules. JTO Clin Res Rep 2022; 3:100299. [PMID: 35392654 PMCID: PMC8980995 DOI: 10.1016/j.jtocrr.2022.100299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 02/06/2022] [Accepted: 02/15/2022] [Indexed: 11/30/2022] Open
Abstract
Introduction Over the years, multiple models have been developed for the evaluation of pulmonary nodules (PNs). This study aimed to comprehensively investigate clinical models for estimating the malignancy probability in patients with PNs. Methods PubMed, EMBASE, Cochrane Library, and Web of Science were searched for studies reporting mathematical models for PN evaluation until March 2020. Eligible models were summarized, and network meta-analysis was performed on externally validated models (PROSPERO database CRD42020154731). The cut-off value of 40% was used to separate patients into high prevalence (HP) and low prevalence (LP), and a subgroup analysis was performed. Results A total of 23 original models were proposed in 42 included articles. Age and nodule size were most often used in the models, whereas results of positron emission tomography-computed tomography were used when collected. The Mayo model was validated in 28 studies. The area under the curve values of four most often used models (PKU, Brock, Mayo, VA) were 0.830, 0.785, 0.743, and 0.750, respectively. High-prevalence group (HP) models had better results in HP patients with a pooled sensitivity and specificity of 0.83 (95% confidence interval [CI]: 0.78–0.88) and 0.71 (95% CI: 0.71–0.79), whereas LP models only achieved pooled sensitivity and specificity of 0.70 (95% CI: 0.60–0.79) and 0.70 (95% CI: 0.62–0.77). For LP patients, the pooled sensitivity and specificity decreased from 0.68 (95% CI: 0.57–0.78) and 0.93 (95% CI: 0.87–0.97) to 0.57 (95% CI: 0.21–0.88) and 0.82 (95% CI: 0.65–0.92) when the model changed from LP to HP models. Compared with the clinical models, artificial intelligence-based models have promising preliminary results. Conclusions Mathematical models can facilitate the evaluation of lung nodules. Nevertheless, suitable model should be used on appropriate cohorts to achieve an accurate result.
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Affiliation(s)
- Kai Zhang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Zihan Wei
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
- Peking University Health Science Center, Beijing, People’s Republic of China
| | - Yuntao Nie
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Haifeng Shen
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Xin Wang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
- Peking University Health Science Center, Beijing, People’s Republic of China
| | - Jun Wang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Kezhong Chen
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
- Corresponding author. Address for correspondence: Kezhong Chen, MD, Department of Thoracic Surgery, Peking University People’s Hospital, Xi Zhi Men South Avenue, Number 11, Beijing 100044, People’s Republic of China.
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13
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Xia C, Liu M, Li X, Zhang H, Li X, Wu D, Ren D, Hua Y, Dong M, Liu H, Chen J. Prediction Model for Lung Cancer in High-Risk Nodules Being Considered for Resection: Development and Validation in a Chinese Population. Front Oncol 2021; 11:700179. [PMID: 34631529 PMCID: PMC8500307 DOI: 10.3389/fonc.2021.700179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 09/06/2021] [Indexed: 01/10/2023] Open
Abstract
Background Determining benign and malignant nodules before surgery is very difficult when managing patients with pulmonary nodules, which further makes it difficult to choose an appropriate treatment. This study aimed to develop a lung cancer risk prediction model for predicting the nature of the nodule in patients’ lungs and deciding whether to perform a surgical intervention. Methods This retrospective study included patients with pulmonary nodules who underwent lobectomy or sublobectomy at Tianjin Medical University General Hospital between 2017 and 2020. All subjects were further divided into training and validation sets. Multivariable logistic regression models with backward selection based on the Akaike information criterion were used to identify independent predictors and develop prediction models. Results To build and validate the model, 503 and 260 malignant and benign nodules were used. Covariates predicting lung cancer in the current model included female sex, age, smoking history, nodule type (pure ground-glass and part-solid), nodule diameter, lobulation, margin (smooth, or spiculated), calcification, intranodular vascularity, pleural indentation, and carcinoembryonic antigen. The final model of this study showed excellent discrimination and calibration with a concordance index (C-index) of 0.914 (0.890–0.939). In an independent sample used for validation, the C-index for the current model was 0.876 (0.825–0.927) compared with 0.644 (0.559–0.728) and 0.681 (0.605–0.757) for the Mayo and Brock models. The decision curve analysis showed that the current model had higher discriminatory power for malignancy than the Mayo and the Brock models. Conclusions The current model can be used in estimating the probability of lung cancer in nodules requiring surgical intervention. It may reduce unnecessary procedures for benign nodules and prompt diagnosis and treatment of malignant nodules.
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Affiliation(s)
- Chunqiu Xia
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghui Liu
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Xin Li
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Hongbing Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Xuanguang Li
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Di Wu
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Dian Ren
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Hua
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Ming Dong
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Hongyu Liu
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Chen
- Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Department of Thoracic Surgery, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
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14
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Schreuder A, Jacobs C, Lessmann N, Broeders MJM, Silva M, Išgum I, de Jong PA, Sverzellati N, Prokop M, Pastorino U, Schaefer-Prokop CM, van Ginneken B. Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening. Eur Respir J 2021; 58:13993003.03386-2020. [PMID: 33574075 DOI: 10.1183/13993003.03386-2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 01/18/2021] [Indexed: 01/17/2023]
Abstract
OBJECTIVES Combined assessment of cardiovascular disease (CVD), COPD and lung cancer may improve the effectiveness of lung cancer screening in smokers. The aims were to derive and assess risk models for predicting lung cancer incidence, CVD mortality and COPD mortality by combining quantitative computed tomography (CT) measures from each disease, and to quantify the added predictive benefit of self-reported patient characteristics given the availability of a CT scan. METHODS A survey model (patient characteristics only), CT model (CT information only) and final model (all variables) were derived for each outcome using parsimonious Cox regression on a sample from the National Lung Screening Trial (n=15 000). Validation was performed using Multicentric Italian Lung Detection data (n=2287). Time-dependent measures of model discrimination and calibration are reported. RESULTS Age, mean lung density, emphysema score, bronchial wall thickness and aorta calcium volume are variables that contributed to all final models. Nodule features were crucial for lung cancer incidence predictions but did not contribute to CVD and COPD mortality prediction. In the derivation cohort, the lung cancer incidence CT model had a 5-year area under the receiver operating characteristic curve of 82.5% (95% CI 80.9-84.0%), significantly inferior to that of the final model (84.0%, 82.6-85.5%). However, the addition of patient characteristics did not improve the lung cancer incidence model performance in the validation cohort (CT model 80.1%, 74.2-86.0%; final model 79.9%, 73.9-85.8%). Similarly, the final CVD mortality model outperformed the other two models in the derivation cohort (survey model 74.9%, 72.7-77.1%; CT model 76.3%, 74.1-78.5%; final model 79.1%, 77.0-81.2%), but not the validation cohort (survey model 74.8%, 62.2-87.5%; CT model 72.1%, 61.1-83.2%; final model 72.2%, 60.4-84.0%). Combining patient characteristics and CT measures provided the largest increase in accuracy for the COPD mortality final model (92.3%, 90.1-94.5%) compared to either other model individually (survey model 87.5%, 84.3-90.6%; CT model 87.9%, 84.8-91.0%), but no external validation was performed due to a very low event frequency. CONCLUSIONS CT measures of CVD and COPD provides small but reproducible improvements to nodule-based lung cancer risk prediction accuracy from 3 years onwards. Self-reported patient characteristics may not be of added predictive value when CT information is available.
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Affiliation(s)
- Anton Schreuder
- Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Colin Jacobs
- Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands.,Fraunhofer MEVIS, Bremen, Germany
| | - Nikolas Lessmann
- Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mireille J M Broeders
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands.,Dutch Expert Centre for Screening, Nijmegen, The Netherlands
| | - Mario Silva
- Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Section of Radiology, Unit of Surgical Sciences, Dept of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pim A de Jong
- Dept of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Nicola Sverzellati
- Section of Radiology, Unit of Surgical Sciences, Dept of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Mathias Prokop
- Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands.,Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Ugo Pastorino
- Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Cornelia M Schaefer-Prokop
- Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands.,Dept of Radiology, Meander Medisch Centrum, Amersfoort, The Netherlands
| | - Bram van Ginneken
- Dept of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands.,Fraunhofer MEVIS, Bremen, Germany.,Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
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15
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Silva M, Milanese G, Ledda RE, Pastorino U, Sverzellati N. Screen-detected solid nodules: from detection of nodule to structured reporting. Transl Lung Cancer Res 2021; 10:2335-2346. [PMID: 34164281 PMCID: PMC8182712 DOI: 10.21037/tlcr-20-296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.
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Affiliation(s)
- Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Roberta E Ledda
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ugo Pastorino
- Section of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milano, Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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16
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Dziadziuszko K, Szurowska E. Pulmonary nodule radiological diagnostic algorithm in lung cancer screening. Transl Lung Cancer Res 2021; 10:1124-1135. [PMID: 33718050 PMCID: PMC7947388 DOI: 10.21037/tlcr-20-755] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Publications of the final results of the two largest randomized lung cancer screening (LCS) trials in the United States and Europe confirmed the reduction in the mortality rate associated with the use of screening with low-dose computed tomography (LDCT). Results of these trials led to widespread acceptance of LCS in properly defined high-risk populations, and its implementation in the clinical practice. Many countries started preparation for national LCS and refreshed still open debate about lung nodule management. Detection of lung cancer in the early stage with a reduction of lung cancer mortality requires dedicated programs with optimized protocols, including a specified pulmonary nodule diagnostic algorithm. The screening protocol should be clear with a precise nodule diameter or volume threshold, based on which a positive screen result is defined. The application of risk-prediction models and the introduction of the semiautomated assessment of detected nodules improves screening accuracy and should be applied in LCS protocols as verified tools to aid radiological diagnosis. In this review, we have summarized recent data about the radiological protocols from the most important LCS programs and pulmonary diagnostic algorithms. These protocols should be taken into consideration in the ongoing and planned LCS programs.
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Affiliation(s)
| | - Edyta Szurowska
- II Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
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17
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Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 2020; 18:135-151. [PMID: 33046839 DOI: 10.1038/s41571-020-00432-6] [Citation(s) in RCA: 192] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2020] [Indexed: 12/17/2022]
Abstract
In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).
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18
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Toumazis I, Bastani M, Han SS, Plevritis SK. Risk-Based lung cancer screening: A systematic review. Lung Cancer 2020; 147:154-186. [DOI: 10.1016/j.lungcan.2020.07.007] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/17/2022]
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Jacob M, Romano J, Araújo D, Pereira JM, Ramos I, Hespanhol V. Predicting lung nodules malignancy. Pulmonology 2020; 28:454-460. [PMID: 32739327 DOI: 10.1016/j.pulmoe.2020.06.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/26/2020] [Accepted: 06/29/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting. METHODS The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy. RESULTS From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573. CONCLUSIONS The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.
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Affiliation(s)
- M Jacob
- Pulmonology Department, Centro Hospitalar Universitário de São João, Porto, Portugal.
| | - J Romano
- Physical Medicine and Rehabilitation Department, Unidade de Saúde Local de Matosinhos, Porto, Portugal
| | - D Araújo
- Pulmonology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - J M Pereira
- Radiology Department, Centro Hospitalar Universitário de São João, Porto, Portugal; Faculty of Medicine of Porto University, Porto, Portugal
| | - I Ramos
- Radiology Department, Centro Hospitalar Universitário de São João, Porto, Portugal; Faculty of Medicine of Porto University, Porto, Portugal
| | - V Hespanhol
- Pulmonology Department, Centro Hospitalar Universitário de São João, Porto, Portugal; Faculty of Medicine of Porto University, Porto, Portugal
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20
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Costantini A, Bostantzoglou C, Blum TG. ERS International Congress, Madrid, 2019: highlights from the Thoracic Oncology Assembly. ERJ Open Res 2020; 6:00131-2020. [PMID: 32714955 PMCID: PMC7369431 DOI: 10.1183/23120541.00131-2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 06/05/2020] [Indexed: 12/18/2022] Open
Abstract
Lung cancer is a devastating disease affecting hundreds of thousands of patients in Europe. Despite recent advances in treatment, its prognosis remains poor. This is mainly attributed to the late stages that diagnoses are usually established at, consequently excluding curative treatment options. During the 2019 European Respiratory Society International Congress in Madrid, Spain, lung cancer experts presented the most recent aspects of lung cancer early detection with low-dose computed tomography. Key thoracic oncology highlights from #ERSCongress Madrid 2019https://bit.ly/3dQZtv7
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Affiliation(s)
- Adrien Costantini
- Service de Pneumologie et d'Oncologie Thoracique, Hôpital Ambroise Paré-AP-HP, Boulogne-Billancourt, France
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21
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Duan S, Cao H, Liu H, Miao L, Wang J, Zhou X, Wang W, Hu P, Qu L, Wu Y. Development of a machine learning-based multimode diagnosis system for lung cancer. Aging (Albany NY) 2020; 12:9840-9854. [PMID: 32445550 PMCID: PMC7288961 DOI: 10.18632/aging.103249] [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: 02/10/2020] [Accepted: 04/20/2020] [Indexed: 02/06/2023]
Abstract
As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broad-spectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.
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Affiliation(s)
- Shuyin Duan
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Huimin Cao
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Hong Liu
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450001, China
| | - Lijun Miao
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450001, China
| | - Jing Wang
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450001, China
| | - Xiaolei Zhou
- Henan Provincial Chest Hospital, Zhengzhou 450001, China
| | - Wei Wang
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB R3E 3N4, Canada
| | - Lingbo Qu
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China.,Henan Joint International Research Laboratory of Green Construction of Functional Molecules and Their Bioanalytical Applications, Zhengzhou 450001, China
| | - Yongjun Wu
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China.,The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou 450001, China
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22
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González Maldonado S, Delorme S, Hüsing A, Motsch E, Kauczor HU, Heussel CP, Kaaks R. Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography. JAMA Netw Open 2020; 3:e1921221. [PMID: 32058555 DOI: 10.1001/jamanetworkopen.2019.21221] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening. OBJECTIVE To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial. DESIGN, SETTING, AND PARTICIPANTS In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from February 1, 2019, through December 5, 2019. INTERVENTIONS Five annual rounds of low-dose multislice CT. MAIN OUTCOMES AND MEASURES Discrimination ability and calibration of malignancy probabilities estimated by 5 models developed in data from screening studies (4 Pan-Canadian Early Detection of Lung Cancer Study [PanCan] models using a parsimonious approach including nodule spiculation [PanCan-1b] or a comprehensive approach including nodule spiculation [PanCan-2b], and PanCan-2b replacing the nodule diameter variable with mean diameter [PanCan-MD] or volume [PanCan-VOL], as well as a model developed by the UK Lung Cancer Screening trial) and 3 models developed in clinical settings (US Department of Veterans Affairs, Mayo Clinic, and Peking University People's Hospital). RESULTS A total of 1159 participants (median [range] age, 57.63 [50.34-71.89] years; 763 [65.8%] men) with 3903 pulmonary nodules were included in this study. For nodules detected in the prevalence round of CT, the PanCan models showed excellent discrimination (PanCan-1b: area under the curve [AUC], 0.93 [95% CI, 0.87-0.99]; PanCan-2b: AUC, 0.94 [95% CI, 0.89-0.99]; PanCan-MD: AUC, 0.94 [95% CI, 0.91-0.98]; PanCan-VOL: AUC, 0.94 [95% CI, 0.90-0.98]), and all of the screening models except PanCan-MD and PanCan-VOL showed acceptable calibration (PanCan-1b: Spiegelhalter z = -1.081; P = .28; PanCan-2b: Spiegelhalter z = 0.436; P = .67; PanCan-MD: Spiegelhalter z = 3.888; P < .001; PanCan-VOL: Spiegelhalter z = 1.978; P = .05; UK Lung Cancer Screening trial: Spiegelhalter z = -1.076; P = .28), whereas the other models showed worse discrimination and calibration, from an AUC of 0.58 (95% CI, 0.46-0.70) for the UK Lung Cancer Screening trial model to an AUC of 0.89 (95% CI, 0.82-0.97) for the Mayo Clinic model. CONCLUSIONS AND RELEVANCE This diagnostic study found that PanCan models showed excellent discrimination and calibration in prevalence screenings, confirming their ability to improve nodule management in screening settings, although calibration to nodules detected in follow-up scans should be improved. The models developed by the Mayo Clinic, Peking University People's Hospital, Department of Veterans Affairs, and UK Lung Cancer Screening Trial did not perform as well.
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Affiliation(s)
- Sandra González Maldonado
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Stefan Delorme
- Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Erna Motsch
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Claus-Peter Heussel
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik-Heidelberg GmbH, Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
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23
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Affiliation(s)
- Marjolein A Heuvelmans
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands
- Department of Pulmonology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Matthijs Oudkerk
- Institute for DiagNostic Accuracy, Groningen, the Netherlands
- University of Groningen, Faculty of Medical Sciences, Groningen, the Netherlands
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Tolwin Y, Gillis R, Agmon IN, Shrem NS, Rosenbaum E, Peled N. Increased Incidence of Lung Cancer Among Patients With Superficial Transitional Cell Carcinoma: A Potential Risk Cohort for Lung Cancer Screening. Clin Lung Cancer 2019; 20:429-434. [PMID: 31303453 DOI: 10.1016/j.cllc.2019.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Revised: 05/27/2019] [Accepted: 06/07/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND Smoking is a major risk factor for lung cancer (LC) and transitional cell carcinoma of the bladder (TCC). Current recommendations for LC screening do not include TCC as a risk factor for determining screening eligibility. In this study we aimed to evaluate whether TCC patients constitute a population who might benefit from LC screening. PATIENTS AND METHODS The Surveillance, Epidemiology, and End Results 18 database was used to determine the incidence, standardized incidence ratio (SIR), and the average time to diagnosis of LC in patients with localized TCC of the bladder (American Joint Committee on Cancer, sixth edition, stages 0-1). RESULTS On the basis of 91,606 patients with localized TCC, The SIR for LC in men was 1.89 (95% confidence interval [CI], 1.8-1.97), significantly different from the risk for all solid tumors. The SIR for LC in women was 2.43 (95% CI, 2.22-2.65), significantly higher than for men. The 5-year incidence of LC was 3.2%, and the 10-year incidence was 5.94%. The average time to diagnosis of LC was 3.4 years, with >80% of LC cases occurring within 5 years of TCC diagnosis. CONCLUSION Patients with localized TCC have a higher incidence of LC than the general population. The risk is significantly increased among women compared with men. Considering this increased risk, patients with early stage TCC might stand to benefit from LC screening. Additional differences were noted between male and female TCC patients, which bear further study.
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Affiliation(s)
| | - Roni Gillis
- The Legacy Heritage Center and Dr Larry Norton Institute, Soroka Medical Center, Beer Sheva, Israel; Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Inbar Nardi Agmon
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Davidoff Cancer Center, Rabin Medical Center, Petach Tiqwa, Israel
| | - Noa Shani Shrem
- The Legacy Heritage Center and Dr Larry Norton Institute, Soroka Medical Center, Beer Sheva, Israel; Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Eli Rosenbaum
- Davidoff Cancer Center, Rabin Medical Center, Petach Tiqwa, Israel
| | - Nir Peled
- The Legacy Heritage Center and Dr Larry Norton Institute, Soroka Medical Center, Beer Sheva, Israel; Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
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