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Heideman BE, Kammer MN, Paez R, Swanson T, Godfrey CM, Low SW, Xiao D, Li TZ, Richardson JR, Knight MA, Shojaee S, Deppen SA, Lentz RJ, Grogan EL, Maldonado F. The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort. CHEST PULMONARY 2024; 2:100033. [PMID: 38737731 PMCID: PMC11087042 DOI: 10.1016/j.chpulm.2023.100033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Abstract
BACKGROUND Pulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical prediction models were developed to inform physician assessment of pretest probability of nodule malignancy but have not been validated in a high-risk cohort of nodules for which biopsy was ultimately performed. RESEARCH QUESTION Do guideline-recommended prediction models sufficiently discriminate between benign and malignant nodules when applied to cases referred for biopsy by navigational bronchoscopy? STUDY DESIGN AND METHODS We assembled a prospective cohort of 322 indeterminate pulmonary nodules in 282 patients referred to a tertiary medical center for diagnostic navigational bronchoscopy between 2017 and 2019. We calculated the probability of malignancy for each nodule using the Brock model, Mayo Clinic model, and Veterans Affairs (VA) model. On a subset of 168 patients who also had PET-CT scans before biopsy, we also calculated the probability of malignancy using the Herder model. The performance of the models was evaluated by calculating the area under the receiver operating characteristic curves (AUCs) for each model. RESULTS The study cohort contained 185 malignant and 137 benign nodules (57% prevalence of malignancy). The malignant and benign cohorts were similar in terms of size, with a median longest diameter for benign and malignant nodules of 15 and 16 mm, respectively. The Brock model, Mayo Clinic model, and VA model showed similar performance in the entire cohort (Brock AUC, 0.70; 95% CI, 0.64-0.76; Mayo Clinic AUC, 0.70; 95% CI, 0.64-0.76; VA AUC, 0.67; 95% CI, 0.62-0.74). For 168 nodules with available PET-CT scans, the Herder model had an AUC of 0.77 (95% CI, 0.68-0.85). INTERPRETATION Currently available clinical models provide insufficient discrimination between benign and malignant nodules in the common clinical scenario in which a patient is being referred for biopsy, especially when PET-CT scan information is not available.
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Affiliation(s)
- Brent E Heideman
- Section of Pulmonary, Critical Care, Allergy and Immunologic Diseases, Atrium Health Wake Forest Baptist, Winston-Salem, NC
| | - Michael N Kammer
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Rafael Paez
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Terra Swanson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Caroline M Godfrey
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - See-Wei Low
- Division of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, OH
| | - David Xiao
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Thomas Z Li
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Jacob R Richardson
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Michael A Knight
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Samira Shojaee
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Stephen A Deppen
- Department of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN; and the Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Robert J Lentz
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Eric L Grogan
- Department of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN; and the Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Fabien Maldonado
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
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Wang H, Li Y, Han J, Lin Q, Zhao L, Li Q, Zhao J, Li H, Wang Y, Hu C. A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer. Front Oncol 2023; 13:1192908. [PMID: 37786508 PMCID: PMC10541960 DOI: 10.3389/fonc.2023.1192908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 09/04/2023] [Indexed: 10/04/2023] Open
Abstract
Objective The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data. Methods A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively. Results The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly. Conclusion The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.
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Affiliation(s)
- Huoqiang Wang
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi Li
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jiexi Han
- Shanghai miRAN Biotech Co. Ltd, Shanghai, China
| | - Qin Lin
- Department of Geriatrics, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Long Zhao
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qiang Li
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Juan Zhao
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Haohao Li
- Faculty of Business and Economics, University of Hong Kong, Hong Kong, China
| | - Yiran Wang
- Shanghai miRAN Biotech Co. Ltd, Shanghai, China
| | - Changlong Hu
- School of Life Sciences, Fudan University, Shanghai, China
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Zheng Y, Dong J, Yang X, Shuai P, Li Y, Li H, Dong S, Gong Y, Liu M, Zeng Q. Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening. Cancer Med 2023. [PMID: 37248730 DOI: 10.1002/cam4.5886] [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: 10/20/2022] [Revised: 03/14/2023] [Accepted: 03/19/2023] [Indexed: 05/31/2023] Open
Abstract
BACKGROUND Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. METHODS The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. RESULTS A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. CONCLUSIONS It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.
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Affiliation(s)
- Yansong Zheng
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jing Dong
- Research of Medical Big Data Center & National Engineering Laboratory for Medical Big Data Application Technology, Chinese PLA General Hospital, Beijing, China
| | - Xue Yang
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Ping Shuai
- Health Management Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongli Li
- Department of Health Management/ Henan Provincial People's Hospital of Zhengzhou University, Henan Key Laboratory of Chronic Disease Management, Zhengzhou, China
| | - Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Shengyong Dong
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yan Gong
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Miao Liu
- Graduate School, Chinese PLA general hospital, Beijing, China
| | - Qiang Zeng
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
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Duarte A, Corbett M, Melton H, Harden M, Palmer S, Soares M, Simmonds M. EarlyCDT Lung blood test for risk classification of solid pulmonary nodules: systematic review and economic evaluation. Health Technol Assess 2022; 26:1-184. [PMID: 36534989 PMCID: PMC9791464 DOI: 10.3310/ijfm4802] [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] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND EarlyCDT Lung (Oncimmune Holdings plc, Nottingham, UK) is a blood test to assess malignancy risk in people with solid pulmonary nodules. It measures the presence of seven lung cancer-associated autoantibodies. Elevated levels of these autoantibodies may indicate malignant disease. The results of the test might be used to modify the risk of malignancy estimated by existing risk calculators, including the Brock and Herder models. OBJECTIVES The objectives were to determine the diagnostic accuracy, clinical effectiveness and cost-effectiveness of EarlyCDT Lung; and to develop a conceptual model and identify evidence requirements for a robust cost-effectiveness analysis. DATA SOURCES MEDLINE (including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE), EMBASE, Cochrane Central Register of Controlled Trials, Science Citation Index, EconLit, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment database, NHS Economic Evaluation Database ( NHS EED ) and the international Health Technology Assessment database were searched on 8 March 2021. REVIEW METHODS A systematic review was performed of evidence on EarlyCDT Lung, including diagnostic accuracy, clinical effectiveness and cost-effectiveness. Study quality was assessed with the quality assessment of diagnostic accuracy studies-2 tool. Evidence on other components of the pulmonary nodule diagnostic pathway (computerised tomography surveillance, Brock risk, Herder risk, positron emission tomography-computerised tomography and biopsy) was also reviewed. When feasible, bivariate meta-analyses of diagnostic accuracy were performed. Clinical outcomes were synthesised narratively. A simulation study investigated the clinical impact of using EarlyCDT Lung. Additional reviews of cost-effectiveness studies evaluated (1) other diagnostic strategies for lung cancer and (2) screening approaches for lung cancer. A conceptual model was developed. RESULTS A total of 47 clinical publications on EarlyCDT Lung were identified, but only five cohorts (695 patients) reported diagnostic accuracy data on patients with pulmonary nodules. All cohorts were small or at high risk of bias. EarlyCDT Lung on its own was found to have poor diagnostic accuracy, with a summary sensitivity of 20.2% (95% confidence interval 10.5% to 35.5%) and specificity of 92.2% (95% confidence interval 86.2% to 95.8%). This sensitivity was substantially lower than that estimated by the manufacturer (41.3%). No evidence on the clinical impact of EarlyCDT Lung was identified. The simulation study suggested that EarlyCDT Lung might potentially have some benefit when considering intermediate risk nodules (10-70% risk) after Herder risk analysis. Two cost-effectiveness studies on EarlyCDT Lung for pulmonary nodules were identified; none was considered suitable to inform the current decision problem. The conceptualisation process identified three core components for a future cost-effectiveness assessment of EarlyCDT Lung: (1) the features of the subpopulations and relevant heterogeneity, (2) the way EarlyCDT Lung test results affect subsequent clinical management decisions and (3) how changes in these decisions can affect outcomes. All reviewed studies linked earlier diagnosis to stage progression and stage shift to final outcomes, but evidence on these components was sparse. LIMITATIONS The evidence on EarlyCDT Lung among patients with pulmonary nodules was very limited, preventing meta-analyses and economic analyses. CONCLUSIONS The evidence on EarlyCDT Lung among patients with pulmonary nodules is insufficient to draw any firm conclusions as to its diagnostic accuracy or clinical or economic value. FUTURE WORK Prospective cohort studies, in which EarlyCDT Lung is used among patients with identified pulmonary nodules, are required to support a future assessment of the clinical and economic value of this test. Studies should investigate the diagnostic accuracy and clinical impact of EarlyCDT Lung in combination with Brock and Herder risk assessments. A well-designed cost-effectiveness study is also required, integrating emerging relevant evidence with the recommendations in this report. STUDY REGISTRATION This study is registered as PROSPERO CRD42021242248. FUNDING This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 49. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Ana Duarte
- Centre for Health Economics, University of York, York UK
| | - Mark Corbett
- Centre for Reviews and Dissemination, University of York, York UK
| | - Hollie Melton
- Centre for Reviews and Dissemination, University of York, York UK
| | - Melissa Harden
- Centre for Reviews and Dissemination, University of York, York UK
| | - Stephen Palmer
- Centre for Health Economics, University of York, York UK
| | - Marta Soares
- Centre for Health Economics, University of York, York UK
| | - Mark Simmonds
- Centre for Reviews and Dissemination, University of York, York UK
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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] [Key Words] [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
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Vayntrub Y, Gartman E, Nici L, Jankowich MD. Diagnostic Performance of the Herder Model in Veterans Undergoing PET Scans for Pulmonary Nodule Evaluation. Lung 2021; 199:653-657. [PMID: 34787695 DOI: 10.1007/s00408-021-00495-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/31/2021] [Indexed: 12/19/2022]
Abstract
British Thoracic Society (BTS) guidelines recommend using the Herder model to risk-stratify lung nodules after positron emission tomography (PET). However, this approach has not been adequately evaluated, particularly among Veterans. A single-center retrospective cohort study was carried out in U.S. Veterans with pulmonary nodules assessed by PET to validate the Herder model; decision analysis using risk thresholds from the BTS guidelines was performed. One hundred subjects met inclusion criteria. Area under the curve of the Herder model for predicting malignancy was 0.87 for all lung nodules and 0.90 for newly discovered nodules. For low- and high-risk lung nodules, BTS guidelines would have recommended appropriate care in this patient cohort.
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Affiliation(s)
- Yevgeniy Vayntrub
- Division of Pulmonary and Critical Care Medicine, Providence VA Medical Center, 830 Chalkstone Avenue, Office 158L, Providence, RI, 02908, USA
- Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Eric Gartman
- Division of Pulmonary and Critical Care Medicine, Providence VA Medical Center, 830 Chalkstone Avenue, Office 158L, Providence, RI, 02908, USA
- Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Linda Nici
- Division of Pulmonary and Critical Care Medicine, Providence VA Medical Center, 830 Chalkstone Avenue, Office 158L, Providence, RI, 02908, USA
- Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Matthew D Jankowich
- Division of Pulmonary and Critical Care Medicine, Providence VA Medical Center, 830 Chalkstone Avenue, Office 158L, Providence, RI, 02908, USA.
- Warren Alpert Medical School of Brown University, Providence, RI, USA.
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The Value of a Seven-Autoantibody Panel Combined with the Mayo Model in the Differential Diagnosis of Pulmonary Nodules. DISEASE MARKERS 2021; 2021:6677823. [PMID: 33688380 PMCID: PMC7914080 DOI: 10.1155/2021/6677823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/26/2021] [Accepted: 02/10/2021] [Indexed: 12/17/2022]
Abstract
Background Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs). Methods The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs was calculated using the Mayo predictive model. The performances of the 7-AAB panel and the Mayo model were analyzed by receiver operating characteristic (ROC) analyses, and the difference between groups was evaluated by chi-square tests (χ2). Results The combined area under the ROC curve (AUC) for all 7 AABs was higher than that of a single one. The sensitivities of the 7-AAB panel were 67.5% in the stage I-II LC patients and 60.3% in the stage III-IV patients, with a specificity of 89.6% for the healthy controls and 83.1% for benign lung disease patients. The detection rate of the 7-AAB panel in the early-stage LC patients was higher than that of traditional tumor markers. The AUC of the 7-AAB panel in combination with the Mayo model was higher than that of the 7-AAB panel alone or the Mayo model alone in distinguishing MPN from benign nodules. For early-stage MPN, the sensitivity and specificity of the combination were 93.5% and 58.0%, respectively. For advanced-stage MPN, the sensitivity and specificity of the combination were 91.4% and 72.8%, respectively. The combination of the 7-AAB panel with the Mayo model significantly improved the detection rate of MPN, but the positive predictive value (PPV) and the specificity were not improved when compared with either the 7-AAB panel alone or the Mayo model alone. Conclusion Our study confirmed the clinical value of the 7-AAB panel for the early detection of lung cancer and in combination with the Mayo model could be used to distinguish benign from malignant pulmonary nodules.
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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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Mourato FA, Brito AET, Romão MSC, Santos RGG, de Almeida CA, de Almeida Filho PJ, Leal ALG. Use of PET/CT to aid clinical decision-making in cases of solitary pulmonary nodule: a probabilistic approach. Radiol Bras 2020; 53:1-6. [PMID: 32313329 PMCID: PMC7159041 DOI: 10.1590/0100-3984.2019.0034] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Objective To determine the frequency with which 18F-FDG-PET/CT findings change the probability of malignancy classification of solitary pulmonary nodules. Materials and Methods This was a retrospective analysis of all 18F-FDG-PET/CT examinations performed for the investigation of a solitary pulmonary nodule between May 2016 and May 2017. We reviewed medical records and PET/CT images to collect the data necessary to calculate the pre-test probability of malignancy using the Swensen model and the Herder model. The probability of malignancy was classified as low if < 5%, intermediate if 5-65%, and high if > 65%. Cases classified as intermediate in the Swensen model were reclassified by the Herder model. Results We reviewed the records for 33 patients, of whom 17 (51.5%) were male. The mean age was 68.63 ± 12.20 years. According to the Swensen model, the probability of malignancy was intermediate in 23 cases (69.7%). Among those, the application of the Herder model resulted in the probability of malignancy being reclassified as low in 6 (26.1%) and as high in 8 (34.8%). Conclusion 18F-FDG-PET/CT was able to modify the probability of malignancy classification of a solitary pulmonary nodule in more than 50% of the cases evaluated.
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Yu M, Wang Z, Yang G, Cheng Y. A model of malignant risk prediction for solitary pulmonary nodules on 18 F-FDG PET/CT: Building and estimating. Thorac Cancer 2020; 11:1211-1215. [PMID: 32162769 PMCID: PMC7180535 DOI: 10.1111/1759-7714.13375] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 02/13/2020] [Accepted: 02/15/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND To develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions. METHODS A total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristics were analyzed between patients with benign SPNs and those with malignant SPNs. Risk factors were screened by multivariate nonconditional logistic regression analysis. The self-verification of the model was performed by receiver operating characteristic (ROC) curve analysis, and out-of-group verification was performed by k-fold cross-validation. RESULTS There were statistically significant differences in age, maximum standardized uptake value (SUVmax ), size, lobulation, spiculation, pleural traction, vessel connection, calcification, presence of vacuoles, and emphysema between patients with benign nodules and those with malignant nodules (all P < 0.05). The risk factors for malignant nodules included age, SUVmax , size, lobulation, calcification and vacuoles. The logistic regression model was as follows: P = l/(1 + e-x ), x = - 5.583 + 0.039 × age + 0.477 × SUVmax + 0.139 × size + 1.537 × lobulation - 1.532 × calcification + 1.113 × vacuole. The estimated area under the curve (AUC) for the model was 0.915 (95% CI: 0.883-0.947), the sensitivity was 89.7%, and the specificity was 78.9%. K-fold cross-validation showed that the training accuracy was 0.899 ± 0.011, and the predictive accuracy was 0.873 ± 0.053. CONCLUSIONS The risk factors for malignant nodules included age, SUVmax , size, lobulation, calcification and vacuoles. After verification, the model has satisfactory accuracy, and it may assist clinics make appropriate treatment decisions.
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Affiliation(s)
- MingMing Yu
- PET/CT Center, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - ZhenGuang Wang
- PET/CT Center, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - GuangJie Yang
- PET/CT Center, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuan Cheng
- PET/CT Center, the Affiliated Hospital of Qingdao University, Qingdao, China
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Marcus MW, Duffy SW, Devaraj A, Green BA, Oudkerk M, Baldwin D, Field J. Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial. Thorax 2019; 74:761-767. [PMID: 31028232 DOI: 10.1136/thoraxjnl-2018-212263] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 01/06/2019] [Accepted: 02/11/2019] [Indexed: 01/09/2023]
Abstract
BACKGROUND Estimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs. METHODS Data from the UK Lung Cancer Screening trial were analysed. Multivariable logistic regression models were used to identify independent predictors and to develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline and at 3-month and 12-month repeat screening. RESULTS Of 1994 participants who underwent CT scan, 1013 participants had a total of 5063 lung nodules and 52 (2.6%) of the participants developed lung cancer during a median follow-up of 4 years. Covariates that predict lung cancer in our model included female gender, asthma, bronchitis, asbestos exposure, history of cancer, early and late onset of family history of lung cancer, smoking duration, FVC, nodule type (pure ground-glass and part-solid) and volume as measured by semiautomated volumetry. The final model incorporating all predictors had excellent discrimination: area under the receiver operating characteristic curve (AUC 0.885, 95% CI 0.880 to 0.889). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected AUC 0.882, 95% CI 0.848 to 0.907). The risk model had a good calibration (goodness-of-fit χ[8] 8.13, p=0.42). CONCLUSIONS Our model may be used in estimating the probability of lung cancer in nodules detected at baseline and at 3 months and 12 months from baseline, allowing more efficient stratification of follow-up in population-based lung cancer screening programmes. TRIAL REGISTRATION NUMBER 78513845.
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Affiliation(s)
- Michael W Marcus
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Stephen W Duffy
- Barts and London, Wolfson Institute of Preventive Medicine, London, UK
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital London, London, UK
| | - Beverley A Green
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Matthijs Oudkerk
- Center for Medical Imaging (CMI), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - John Field
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
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12
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Winter A, Aberle DR, Hsu W. External validation and recalibration of the Brock model to predict probability of cancer in pulmonary nodules using NLST data. Thorax 2019; 74:551-563. [PMID: 30898897 DOI: 10.1136/thoraxjnl-2018-212413] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 01/29/2019] [Accepted: 02/04/2019] [Indexed: 12/23/2022]
Abstract
INTRODUCTION We performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement. We report how external validation results can be interpreted and highlight the role of recalibration and model updating. MATERIALS AND METHODS We assessed model discrimination and calibration using the NLST data set. Adhering to the inclusion/exclusion criteria reported by McWilliams et al, we identified 7879 non-calcified nodules discovered at the baseline low-dose CT screen with 2 years of follow-up. We characterised differences between Pan-Canadian Early Detection of Lung Cancer Study and NLST cohorts. We calculated the slope on the prognostic index and the intercept coefficient by fitting the original Brock model to NLST. We also assessed the impact of model recalibration and the addition of new covariates such as body mass index, smoking status, pack-years and asbestos. RESULTS While the area under the curve (AUC) of the model was good, 0.905 (95% CI 0.882 to 0.928), a histogram plot showed that the model poorly differentiated between benign and malignant cases. The calibration plot showed that the model overestimated the probability of cancer. In recalibrating the model, the coefficients for emphysema, spiculation and nodule count were updated. The updated model had an improved calibration and achieved an optimism-corrected AUC of 0.912 (95% CI 0.891 to 0.932). Only pack-year history was found to be significant (p<0.01) among the new covariates evaluated. CONCLUSION While the Brock model achieved a high AUC when validated on the NLST data set, the model benefited from updating and recalibration. Nevertheless, covariates used in the model appear to be insufficient to adequately discriminate malignant cases.
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Affiliation(s)
- Audrey Winter
- Department of Radiological Sciences, Medical Imaging Informatics, University of California, Los Angeles, California, USA
| | - Denise R Aberle
- Department of Radiological Sciences, Medical Imaging Informatics, University of California, Los Angeles, California, USA
| | - William Hsu
- Department of Radiological Sciences, Medical Imaging Informatics, University of California, Los Angeles, California, USA
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Cost-effectiveness of second-line diagnostic investigations in patients included in the DANTE trial: a randomized controlled trial of lung cancer screening with low-dose computed tomography. Nucl Med Commun 2019; 40:508-516. [PMID: 30875336 DOI: 10.1097/mnm.0000000000000993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
AIM The aim of this study was to analyze the economic efficiency of second-line diagnostic investigations in patients with undetermined lung nodules. PARTICIPANTS AND METHODS A retrospective review of all surgical cases included in the DANTE trial from 2001 to 2006 for lung cancer screening was performed. Overall, 217 patients and 261 lung nodules were analyzed. The cohort was divided into patients investigated with PET and/or computed tomography (CT)-guided biopsy (PET-CTB protocol; N=100), compared with those assessed with serial low-dose CT scans (standard protocol; N=161). Outpatient's and inpatient's costs were expressed in euros and derived from the Italian National Health Service. Ineffective costs were defined as the cost of procedures that lead to avoidable surgical intervention. RESULTS The diagnostic accuracy of the two protocols was 91% for the standard (sensitivity 100%, specificity 91%, positive predictive value 26%, and negative predictive value 100%) and 90% for the PET-CTB protocol (sensitivity 98%, specificity 81%, positive predictive value 85%, and negative predictive value 97%). Average costs for outpatient's diagnostics were 694 and 1.462 euros, respectively, for the standard and PET-CTB protocol. Average inpatient's costs for both protocols were 12.121 euros. The two protocols showed comparable effectiveness in terms of outpatient's costs (94 and 90%, respectively; P=0.252). Inpatient's costs were effective in 36% of cases monitored according to the standard protocol compared with 85% of patients investigated with PET-CTB protocol. Ineffective costs corresponded to 64 and 15%, respectively (P<0.0001). CONCLUSION Despite a higher average cost for outpatient's diagnostics, the implementation of PET imaging with or without CT-guided needle biopsy in the workup of suspicious lung nodules results in reduced unnecessary harm and costs related to inpatient's procedures.
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Cheng YI, Davies MPA, Liu D, Li W, Field JK. Implementation planning for lung cancer screening in China. PRECISION CLINICAL MEDICINE 2019; 2:13-44. [PMID: 35694700 PMCID: PMC8985785 DOI: 10.1093/pcmedi/pbz002] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/19/2018] [Accepted: 12/24/2018] [Indexed: 02/05/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in China, with over 690 000 lung cancer deaths estimated in 2018. The mortality has increased about five-fold from the mid-1970s to the 2000s. Lung cancer low-dose computerized tomography (LDCT) screening in smokers was shown to improve survival in the US National Lung Screening Trial, and more recently in the European NELSON trial. However, although the predominant risk factor, smoking contributes to a lower fraction of lung cancers in China than in the UK and USA. Therefore, it is necessary to establish Chinese-specific screening strategies. There have been 23 associated programmes completed or still ongoing in China since the 1980s, mainly after 2000; and one has recently been planned. Generally, their entry criteria are not smoking-stringent. Most of the Chinese programmes have reported preliminary results only, which demonstrated a different high-risk subpopulation of lung cancer in China. Evidence concerning LDCT screening implementation is based on results of randomized controlled trials outside China. LDCT screening programmes combining tobacco control would produce more benefits. Population recruitment (e.g. risk-based selection), screening protocol, nodule management and cost-effectiveness are discussed in detail. In China, the high-risk subpopulation eligible for lung cancer screening has not as yet been confirmed, as all the risk parameters have not as yet been determined. Although evidence on best practice for implementation of lung cancer screening has been accumulating in other countries, further research in China is urgently required, as China is now facing a lung cancer epidemic.
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Affiliation(s)
- Yue I Cheng
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
| | - Michael P A Davies
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
| | - Dan Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - John K Field
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
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Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules. Ann Am Thorac Soc 2018; 15:1117-1126. [DOI: 10.1513/annalsats.201803-173cme] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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16
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Yang B, Jhun BW, Shin SH, Jeong BH, Um SW, Zo JI, Lee HY, Sohn I, Kim H, Kwon OJ, Lee K. Comparison of four models predicting the malignancy of pulmonary nodules: A single-center study of Korean adults. PLoS One 2018; 13:e0201242. [PMID: 30063725 PMCID: PMC6067755 DOI: 10.1371/journal.pone.0201242] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 07/11/2018] [Indexed: 12/26/2022] Open
Abstract
Objective Four commonly used clinical models for predicting the probability of malignancy in pulmonary nodules were compared. While three of the models (Mayo Clinic, Veterans Association [VA], and Brock University) are based on clinical and computed tomography (CT) characteristics, one model (Herder) additionally includes the 18F-fluorodeoxyglucose (FDG) uptake value among the positron emission tomography (PET) characteristics. This study aimed to compare the predictive power of these four models in the context of a population drawn from a single center in an endemic area for tuberculosis in Korea. Methods A retrospective analysis of 242 pathologically confirmed nodules (4–30 mm in diameter) in 242 patients from January 2015 to December 2015 was performed. The area under the receiver operating characteristic curve (AUC) was used to assess the predictive performance with respect to malignancy. Results Of 242 nodules, 187 (77.2%) were malignant and 55 (22.8%) were benign, with tuberculosis granuloma being the most common type of benign nodule (23/55). PET was performed for 227 nodules (93.8%). The Mayo, VA, and Brock models showed similar predictive performance for malignant nodules (AUC: 0.6145, 0.6042 and 0.6820, respectively). The performance of the Herder model (AUC: 0.5567) was not significantly different from that of the Mayo (vs. Herder, p = 0.576) or VA models (vs. Herder, p = 0.999), and there were no differences among the three models in determining the probability of malignancy of pulmonary nodules. However, compared with the Brock model, the Herder model showed a significantly lower ability to predict malignancy (adjusted p = 0.0132). Conclusions In our study, the Herder model including the 18FDG uptake value did not perform better than the other models in predicting malignant nodules, suggesting the limited utility of adding PET/CT data to models predicting malignancy in populations within endemic areas for benign inflammatory nodules, such as tuberculosis.
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Affiliation(s)
- Bumhee Yang
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Byung Woo Jhun
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sun Hye Shin
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Byeong-Ho Jeong
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jae Il Zo
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ho Yun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Insoek Sohn
- Statistics and Data Center, Samsung Medical Center, Seoul, Korea
| | - Hojoong Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - O. Jung Kwon
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Kyungjong Lee
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- * E-mail: ,
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Divisi D, Barone M, Bertolaccini L, Zaccagna G, Gabriele F, Crisci R. Diagnostic performance of fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a meta-analysis. J Thorac Dis 2018; 10:S779-S789. [PMID: 29780624 DOI: 10.21037/jtd.2017.12.126] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background In the setting of solitary pulmonary nodules (SPNs), fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) is considered a useful non-invasive diagnostic tool though false positive (FP) and false negative (FN) results affects accuracy due to different conditions, such as inflammatory diseases or low-uptake neoplasms. Aim of this study is to evaluate overall diagnostic performance of 18F-FDG-PET/CT for malignant pulmonary nodules. Methods A computerized research, including published articles from 2012 and 2017, was carried out. 18F-FDG-PET/CT overall sensitivity (Se), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR), positive predictive value (PPV), negative predictive value (NPV), diagnostic index and odds ratio were pooled. No selection-bias were found according to asymmetry test. Results A total of twelve studies were included in the meta-analysis. The pooled Se, Spe, PLR, NLR, PPV, NPV and accuracy index (AI) with relative 95% confidence intervals (CI) were 0.819 (95% CI: 0.794-0.843), 0.624 (95% CI: 0.582-0.665), 2.190 (95% CI: 1.950-2.440), 0.290 (95% CI: 0.250-0.330), 0.802 (95% CI: 0.783-0.819), 0.652 (95% CI: 0.618-0.684) and 0.649 (95% CI: 0.625-0.673), respectively. The diagnostic odds ratio (DOR) was 7.049 with a relative 95% CI between 5.550 and 8.944. Conclusions The results suggest 18F-FDG-PET/CT has good diagnostic accuracy in SPNs evaluation; but, it should not be considered as a discriminatory test rather than a method to be included in a clinical and diagnostic pathway.
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Affiliation(s)
- Duilio Divisi
- Thoracic Surgery Unit, University of L'Aquila, "G. Mazzini" Hospital, Teramo Italy
| | - Mirko Barone
- Thoracic Surgery Unit, University of L'Aquila, "G. Mazzini" Hospital, Teramo Italy
| | | | - Gino Zaccagna
- Thoracic Surgery Unit, University of L'Aquila, "G. Mazzini" Hospital, Teramo Italy
| | - Francesca Gabriele
- Thoracic Surgery Unit, University of L'Aquila, "G. Mazzini" Hospital, Teramo Italy
| | - Roberto Crisci
- Thoracic Surgery Unit, University of L'Aquila, "G. Mazzini" Hospital, Teramo Italy
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Tanner NT, Porter A, Gould MK, Li XJ, Vachani A, Silvestri GA. Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation. Chest 2017; 152:263-270. [PMID: 28115167 DOI: 10.1016/j.chest.2017.01.018] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 11/25/2016] [Accepted: 01/02/2017] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The annual incidence of pulmonary nodules is estimated at 1.57 million. Guidelines recommend using an initial assessment of nodule probability of malignancy (pCA). A previous study found that despite this recommendation, physicians did not follow guidelines. METHODS Physician assessments (N = 337) and two previously validated risk model assessments of pretest probability of cancer were evaluated for performance in 337 patients with pulmonary nodules based on final diagnosis and compared. Physician-assessed pCA was categorized into low, intermediate, and high risk, and the next test ordered was evaluated. RESULTS The prevalence of malignancy was 47% (n = 158) at 1 year. Physician-assessed pCA performed better than nodule prediction calculators (area under the curve, 0.85 vs 0.75; P < .001 and .78; P = .0001). Physicians did not follow indicated guidelines when selecting the next test in 61% of cases (n = 205). Despite recommendations for serial CT imaging in those with low pCA, 52% (n = 13) were managed more aggressively with PET imaging or biopsy; 12% (n = 3) underwent biopsy procedures for benign disease. Alternatively, in the high-risk category, the majority (n = 103 [75%]) were managed more conservatively. Stratified by diagnosis, 92% (n = 22) with benign disease underwent more conservative management with CT imaging (20%), PET scanning (15%), or biopsy (8%), although three had surgery (8%). CONCLUSIONS Physician assessment as a means for predicting malignancy in pulmonary nodules is more accurate than previously validated nodule prediction calculators. Despite the accuracy of clinical intuition, physicians did not follow guideline-based recommendations when selecting the next diagnostic test. To provide optimal patient care, focus in the areas of guideline refinement, implementation, and dissemination is needed.
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Affiliation(s)
- Nichole T Tanner
- Thoracic Oncology Research Group, Division of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, SC; Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Veterans Affairs Hospital, Charleston, SC.
| | | | | | | | - Anil Vachani
- Pulmonary, Allergy, and Critical Care Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gerard A Silvestri
- Thoracic Oncology Research Group, Division of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, SC
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