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Jhala K, Byrne SC, Hammer MM. Interpreting Lung Cancer Screening CTs: Practical Approach to Lung Cancer Screening and Application of Lung-RADS. Clin Chest Med 2024; 45:279-293. [PMID: 38816088 DOI: 10.1016/j.ccm.2023.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Lung cancer screening via low-dose computed tomography (CT) reduces mortality from lung cancer, and eligibility criteria have recently been expanded to include patients aged 50 to 80 with at least 20 pack-years of smoking history. Lung cancer screening CTs should be interepreted with use of Lung Imaging Reporting and Data System (Lung-RADS), a reporting guideline system that accounts for nodule size, density, and growth. The revised version of Lung-RADS includes several important changes, such as expansion of the definition of juxtapleural nodules, discussion of atypical pulmonary cysts, and stepped management for suspicious nodules. By using Lung-RADS, radiologists and clinicians can adopt a uniform approach to nodules detected during CT lung cancer screening and reduce false positives.
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Affiliation(s)
- Khushboo Jhala
- Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02215, USA
| | - Suzanne C Byrne
- Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02215, USA
| | - Mark M Hammer
- Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02215, USA.
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2
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Susai CJ, Velotta JB, Sakoda LC. Clinical Adjuncts to Lung Cancer Screening: A Narrative Review. Thorac Surg Clin 2023; 33:421-432. [PMID: 37806744 PMCID: PMC10926946 DOI: 10.1016/j.thorsurg.2023.03.002] [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: 10/10/2023]
Abstract
The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.
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Affiliation(s)
- Cynthia J Susai
- UCSF East Bay General Surgery, 1411 East 31st Street QIC 22134, Oakland, CA 94612, USA
| | - Jeffrey B Velotta
- Department of Thoracic Surgery, Kaiser Permanente Northern California, 3600 Broadway, Oakland, CA 94611, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA.
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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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Cui X, Zheng S, Zhang W, Fan S, Wang J, Song F, Liu X, Zhu W, Ye Z. Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT. Eur Radiol 2023:10.1007/s00330-023-09432-3. [PMID: 36723725 DOI: 10.1007/s00330-023-09432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/31/2022] [Accepted: 01/02/2023] [Indexed: 02/02/2023]
Abstract
OBJECTIVES This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT. METHODS A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately. RESULTS Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts. CONCLUSIONS We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment. KEY POINTS • Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions. • Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.
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Affiliation(s)
- Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Sunyi Zheng
- Artificial Intelligence and Biomedical Image Analysis Lab, School of Engineering, Westlake University, Hangzhou, People's Republic of China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, People's Republic of China
| | - Wenjia Zhang
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Shuxuan Fan
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Jing Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, People's Republic of China
| | - Feipeng Song
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Xu Liu
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Weijie Zhu
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Zhaoxiang Ye
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China.
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5
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Lindsay WD, Sachs N, Gee JC, Mortani Barbosa EJ. Transparent Machine Learning Models to Diagnose Suspicious Thoracic Lesions Leveraging CT Guided Biopsy Data. Acad Radiol 2022; 29 Suppl 2:S156-S164. [PMID: 34373194 DOI: 10.1016/j.acra.2021.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 06/28/2021] [Accepted: 07/03/2021] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES To train and validate machine learning models capable of classifying suspicious thoracic lesions as benign or malignant and to further classify malignant lesions by pathologic subtype while quantifying feature importance for each classification. MATERIALS AND METHODS 796 patients who had undergone CT guided thoracic biopsy for a concerning thoracic lesion (79.3% lung, 11.4% mediastinum, 6.5% pleura, 2.7% chest wall) were retrospectively enrolled. Lesions were classified as malignant or benign based on ground-truth pathology result, and malignant lesions were classified as primary or secondary cancer. Clinical variables were extracted from EMR and radiology reports. Supervised binary and multiclass classification models were trained to classify lesions based on the input features and evaluated on a held-out test set. Model specific feature analyses were performed to identify variables most predictive of each class, as well as to assess the independent importance of clinical, and imaging features. RESULTS Binary classification models achieved a top accuracy of 80.6%, with predictive features included smoking history, age, lesion size, and lesion location. Multiclass classification models achieved a top weighted average f1-score of 0.73. Features predictive of primary cancer included smoking history, race, and age, while features predictive of secondary cancer included lesion location, and a history of cancer. CONCLUSION Machine learning models enable classification of suspicious thoracic lesions based on clinical and imaging variables, achieving clinically useful performance while identifying importance of individual input features on a pathology-proven dataset. We believe models such as these are more likely to be trusted and adopted by clinicians.
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Affiliation(s)
- William D Lindsay
- Perelman School of Medicine, University of Pennsylvania Health System, Philadelphia, Pennsylvania; Department of Bioengineering, School of Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Nicholas Sachs
- Perelman School of Medicine, University of Pennsylvania Health System, Philadelphia, Pennsylvania
| | - James C Gee
- Department of Bioengineering, School of Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Eduardo J Mortani Barbosa
- Department of Bioengineering, School of Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania.
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6
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Senent-Valero M, Librero J, Pastor-Valero M. Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review. Syst Rev 2021; 10:308. [PMID: 34872592 PMCID: PMC8650360 DOI: 10.1186/s13643-021-01856-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 11/18/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice. METHODS We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. RESULTS A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice. CONCLUSIONS The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42020161559.
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Affiliation(s)
- Marina Senent-Valero
- Department of Public Health, History of Science and Gynaecology, Faculty of Medicine, Miguel Hernández University, Sant Joan d’Alacant, Alicante, Spain
| | - Julián Librero
- Navarrabiomed, Complejo Hospitalario de Navarra, UPNA, Pamplona, Spain
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain
| | - María Pastor-Valero
- Department of Public Health, History of Science and Gynaecology, Faculty of Medicine, Miguel Hernández University, Sant Joan d’Alacant, Alicante, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
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7
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Kinsey CM, Billatos E, Mori V, Tonelli B, Cole BF, Duan F, Marques H, de la Bruere I, Onieva J, San José Estépar R, Cleveland A, Idelkope D, Stevenson C, Bates JHT, Aberle D, Spira A, Washko G, San José Estépar R. A simple assessment of lung nodule location for reduction in unnecessary invasive procedures. J Thorac Dis 2021; 13:4207-4216. [PMID: 34422349 PMCID: PMC8339782 DOI: 10.21037/jtd-20-3093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/23/2021] [Indexed: 12/05/2022]
Abstract
Background CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy. Methods We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4–20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium. Results The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign. Conclusions Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures. Trial Registration NCT00047385, NCT01785342.
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Affiliation(s)
- C Matthew Kinsey
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | - Ehab Billatos
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, Boston Medical Center, Boston, MA, USA
| | - Vitor Mori
- University of Sao Paolo, Sao Paolo, Brazil
| | | | - Bernard F Cole
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA
| | - Fenghai Duan
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
| | - Helga Marques
- Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
| | | | - Jorge Onieva
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | | | | | - Dan Idelkope
- Geisel School of Medicine at Dartmouth College, Hanover, NH, USA
| | | | - Jason H T Bates
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | - Denise Aberle
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Avi Spira
- The Pulmonary Unit, Boston Medical Center, Boston, MA, USA
| | - George Washko
- Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA
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8
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Gupta S, Jacobson FL, Kong CY, Hammer MM. Performance of Lung Nodule Management Algorithms for Lung-RADS Category 4 Lesions. Acad Radiol 2021; 28:1037-1042. [PMID: 32540198 DOI: 10.1016/j.acra.2020.04.041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 04/30/2020] [Accepted: 04/30/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE To test the performance of the American College of Chest Physicians (ACCP) and British Thoracic Society (BTS) algorithms to stratify high-risk nodules identified at lung cancer screening. METHOD AND MATERIALS Patients with Lung-RADS category 4 nodules identified on lung cancer screening computed tomography (CT) between March 2014 and August 2018 were identified, and a subset of 150 were randomly selected. Nodule characteristics and, if available, fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET)-CT scan were recorded. Radiologists blinded to final diagnosis and downstream testing performed five-point visual assessment score for probability of nodule malignancy; their accuracies are averaged below. Probabilities of malignancy according to Brock and Herder models were calculated. ACCP and BTS algorithms were applied to the nodules. RESULTS Final diagnosis of malignancy was made in 65/150 (43%) of patients. The sensitivity, specificity and accuracy for nodule malignancy were: radiologist visual score (92%, 85%, 88%); BTS (76%, 91%, 85%); ACCP (63%, 89%, 78%); and Brock calculator (77%, 71%, 73%). The sensitivity, specificity, and accuracy for nodule malignancy in patients with FDG PET-CT scan (n = 78) were: FDG uptake (91%, 64%, 83%); Herder probability (91%, 68%, 83%); radiologist visual score (93%, 69%, 86%); BTS (84%, 64%, 78%); Brock probability (82%, 50%, 72%); and ACCP (68%, 59%, 65%). CONCLUSION Thoracic radiologist visual analysis yielded the greatest accuracy for nodule triage in the entire cohort. BTS performed better than ACCP guidelines and both performed better than the Brock model alone.
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9
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Munden RF, Black WC, Hartman TE, MacMahon H, Ko JP, Dyer DS, Naidich D, Rossi SE, McAdams HP, Goodman EM, Brown K, Kent M, Carter BW, Chiles C, Leung AN, Boiselle PM, Kazerooni EA, Berland LL, Pandharipande PV. Managing Incidental Findings on Thoracic CT: Lung Findings. A White Paper of the ACR Incidental Findings Committee. J Am Coll Radiol 2021; 18:1267-1279. [PMID: 34246574 DOI: 10.1016/j.jacr.2021.04.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 04/14/2021] [Indexed: 12/17/2022]
Abstract
The ACR Incidental Findings Committee presents recommendations for managing incidentally detected lung findings on thoracic CT. The Chest Subcommittee is composed of thoracic radiologists who endorsed and developed the provided guidance. These recommendations represent a combination of current published evidence and expert opinion and were finalized by informal iterative consensus. The recommendations address commonly encountered incidental findings in the lungs and are not intended to be a comprehensive review of all pulmonary incidental findings. The goal is to improve the quality of care by providing guidance on management of incidentally detected thoracic findings.
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Affiliation(s)
- Reginald F Munden
- Professor, Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina; Chair, Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - William C Black
- Professor of Radiology, Emeritus, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | | | - Heber MacMahon
- Professor of Radiology, Section of Thoracic Imaging, Department of Radiology, The University of Chicago, Chicago, Illinois
| | - Jane P Ko
- Professor of Radiology, Department of Radiology, NYU Langone Health, New York, New York; Fellowship Director, Cardiothoracic Imaging, Department of Radiology, NYU Langone Health, New York, New York
| | - Debra S Dyer
- Professor, Department of Radiology, National Jewish Health, Denver, Colorado; Chair, Department of Radiology, National Jewish Health, Denver, Colorado
| | - David Naidich
- Professor, Emeritus, NYU-Langone Health, New York, New York; Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Santiago E Rossi
- Chairman, Centro Rossi, Buenos Aires, Argentina; Chest Section Head, Hospital Cetrángolo, Buenos Aires, Argentina
| | - H Page McAdams
- Professor of Radiology, Duke University Health System, Durham, North Carolina
| | - Eric M Goodman
- Assistant Professor, Department of Radiology, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York; Associate Program Director, Diagnostic Radiology, Department of Radiology, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York
| | - Kathleen Brown
- Professor, Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, California; Section Chief, Thoracic Imaging, Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, California; Assistant Dean, Equity and Diversity Inclusion, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Michael Kent
- Associate Professor of Surgery, Harvard Medical School, Boston, Massachusetts; Director, Minimally Invasive Thoracic Surgery, Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Brett W Carter
- Associate Professor, Department of Thoracic Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas; Director of Clinical Operations, University of Texas MD Anderson Cancer Center, Houston, Texas; Chief Patient Safety and Quality Officer, Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Caroline Chiles
- Professor, Department of Radiology, Wake Forest Baptist Health, Winston Salem, North Carolina
| | - Ann N Leung
- Professor, Clinical Affairs, Stanford University Medical Center, Stanford, California; Associate Chair, Clinical Affairs, Stanford University Medical Center, Stanford, California; Department of Radiology, Stanford University Medical Center, Stanford, California
| | - Phillip M Boiselle
- Professor, Quinnipiac's Frank H. Netter MD School of Medicine, North Haven, Connecticut; Dean, Quinnipiac's Frank H. Netter MD School of Medicine, William and Barbara Weldon Dean's Chair of Medicine, North Haven, Connecticut
| | - Ella A Kazerooni
- Professor of Radiology, Division of Cardiothoracic Radiology and Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Michigan
| | - Lincoln L Berland
- Professor Emeritus, University of Alabama at Birmingham, Birmingham, Alabama
| | - Pari V Pandharipande
- Director, MGH Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts; Associate Chair, Integrated Imaging & Imaging Sciences, MGH Radiology, Massachusetts General Hospital, Boston, Massachusetts; Executive Director, Clinical Enterprise Integration, Mass General Brigham (MGB) Radiology, Massachusetts General Hospital, Boston, Massachusetts; Associate Professor of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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10
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Hammer MM, Gupta S, Kong CY. Cost-Effectiveness of Management Algorithms for Lung-RADS Category 4 Nodules. Radiol Cardiothorac Imaging 2021; 3:e200523. [PMID: 33969309 PMCID: PMC8098088 DOI: 10.1148/ryct.2021200523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 12/21/2020] [Accepted: 01/04/2021] [Indexed: 06/12/2023]
Abstract
PURPOSE To evaluate nodule management guidelines in a simulated cohort of Lung Reporting and Data System (Lung-RADS) 4 nodules based on real-world data. MATERIALS AND METHODS In this retrospective study, 100 000 patients were simulated from 151 patients with Lung-RADS 4 nodules (from January 2010 to August 2018). Each patient in the simulation was managed with each algorithm, and health outcomes were accumulated based on interventions and delays to cancer diagnosis. If the algorithm missed a cancer, it was diagnosed at the next annual screening round, although it would grow in the interim. Patient age-specific or cancer-specific mortality was assigned depending on whether the nodule was malignant, and quality-adjusted life years (QALYs) were calculated. Costs of interventions and cancer treatment were accumulated. One-way sensitivity analyses were performed. RESULTS The most effective algorithm was the British Thoracic Society (BTS; 10.041 QALYs), followed by the American College of Chest Physicians (10.035 QALYs) and Lung-RADS (10.021 QALYs). Only the BTS and Lung-RADS were on the efficient frontier, with an incremental cost-effectiveness ratio (ICER) of $52 634 (95% CI: $45 122, $60 619). Under nearly all sensitivity analyses, the only algorithms on the efficient frontier were BTS and Lung-RADS. The ICERs for BTS versus Lung-RADS were under $100 000 for all scenarios except an increased life expectancy in patients without cancer, in which case the ICER was $109 273. CONCLUSION The BTS algorithm and Lung-RADS were cost-effective for managing category 4 nodules, with BTS yielding greater QALYs.Supplemental material is available for this article.© RSNA, 2021See also the commentary by Elicker in this issue.
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11
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Dyer SC, Bartholmai BJ, Koo CW. Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening. J Thorac Dis 2020; 12:6966-6977. [PMID: 33282402 PMCID: PMC7711402 DOI: 10.21037/jtd-2019-cptn-02] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Lung cancer remains the leading cause of cancer death in the United States. Screening with low-dose computed tomography (LDCT) has been proven to aid in early detection of lung cancer and reduce disease specific mortality. In 2014, the American College of Radiology (ACR) released version 1.0 of the Lung CT Screening Reporting and Data System (Lung-RADS) as a quality tool to standardize the reporting of lung cancer screening LDCT. In 2019, 5 years after the implementation of Lung-RADS version 1.0 the ACR released the updated Lung-RADS version 1.1 which incorporates initial experience with lung cancer screening. In this review, we outline the implications of the changes and additions in Lung-RADS version 1.1 and examine relevant literature for many of the updates. We also highlight several challenges and opportunities as Lung-RADS version 1.1 is implemented in lung cancer screening programs.
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Affiliation(s)
- Spencer C Dyer
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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12
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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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13
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Cui X, Heuvelmans MA, Han D, Zhao Y, Fan S, Zheng S, Sidorenkov G, Groen HJM, Dorrius MD, Oudkerk M, de Bock GH, Vliegenthart R, Ye Z. Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population. Transl Lung Cancer Res 2019; 8:605-613. [PMID: 31737497 DOI: 10.21037/tlcr.2019.09.17] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients. Methods This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference. Results In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models. Conclusions In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable.
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Affiliation(s)
- Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Centre of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.,Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marjolein A Heuvelmans
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department of Pulmonology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Daiwei Han
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Yingru Zhao
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Centre of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Shuxuan Fan
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Centre of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Sunyi Zheng
- Department of Radiotherapy, Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
| | - Grigory Sidorenkov
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Harry J M Groen
- Department of Pulmonary Diseases, Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
| | - Monique D Dorrius
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.,i-DNA BV, Groningen, The Netherlands
| | - Geertruida H de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Zhaoxiang Ye
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Centre of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
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14
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Uthoff J, Koehn N, Larson J, Dilger SKN, Hammond E, Schwartz A, Mullan B, Sanchez R, Hoffman RM, Sieren JC. Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant? Eur Radiol 2019; 29:5367-5377. [PMID: 30937590 DOI: 10.1007/s00330-019-06168-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 02/06/2019] [Accepted: 03/14/2019] [Indexed: 12/19/2022]
Abstract
OBJECTIVES Post-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally. MATERIALS AND METHODS A common cohort of 317 individuals with computed tomography-detected, solid nodules (80 malignant, 237 benign) were used to evaluate the MPM performance. We created a web-based application for this study that allows others to easily calibrate thresholds and analyze the performance of MPMs on their local cohort. Thirty patients with repeated imaging were tested for improved performance longitudinally. RESULTS Using calibrated thresholds, Mayo Clinic and Brock University (BU) MPMs performed the best (AUC = 0.63, 0.61) compared to the Veteran's Affairs (0.51) and Peking University (0.55). Only BU had consensus with the original MPM threshold; the other calibrated thresholds improved MPM accuracy. No significant improvements in accuracy were found longitudinally between time points. CONCLUSIONS Calibration to a common cohort can select the best-performing MPM for your institution. Without calibration, BU has the most stable performance in solid nodules ≥ 8 mm but has only moderate potential to refine subjects into appropriate workup. Application of MPM is recommended only at initial evaluation as no increase in accuracy was achieved over time. KEY POINTS • Post-imaging lung cancer risk mathematical predication models (MPMs) perform poorly on local populations without calibration. • An application is provided to facilitate calibration to new study cohorts: the Mayo Clinic model, the U.S. Department of Veteran's Affairs model, the Brock University model, and the Peking University model. • No significant improvement in risk prediction occurred in nodules with repeated imaging sessions, indicating the potential value of risk prediction application is limited to the initial evaluation.
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Affiliation(s)
- Johanna Uthoff
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA.,Department of Biomedical Engineering, University of Iowa, 5601 Seamans Center, Iowa City, IA, 52242, USA
| | - Nicholas Koehn
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA
| | - Jared Larson
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA
| | - Samantha K N Dilger
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA.,Department of Biomedical Engineering, University of Iowa, 5601 Seamans Center, Iowa City, IA, 52242, USA
| | - Emily Hammond
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA.,Department of Biomedical Engineering, University of Iowa, 5601 Seamans Center, Iowa City, IA, 52242, USA
| | - Ann Schwartz
- Karmanos Cancer Institute, Wayne State University, 4100 John R St, Detroit, MI, 48201, USA
| | - Brian Mullan
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA
| | - Rolando Sanchez
- Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Richard M Hoffman
- Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Jessica C Sieren
- Department of Radiology, University of Iowa, 200 Hawkins Drive cc704 GH, Iowa City, IA, 52242, USA. .,Department of Biomedical Engineering, University of Iowa, 5601 Seamans Center, Iowa City, IA, 52242, USA.
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Black WC. The Complementary Roles of the Vancouver Risk Calculator and Lung-RADS in Lung Cancer Screening. Radiology 2019; 291:212-213. [DOI: 10.1148/radiol.2019182891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- William C. Black
- From the Department of Radiology, Dartmouth-Hitchcock Medical Center, One Medical Center Dr, Lebanon, NH 03756
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16
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White CS, Dharaiya E, Dalal S, Chen R, Haramati LB. Vancouver Risk Calculator Compared with ACR Lung-RADS in Predicting Malignancy: Analysis of the National Lung Screening Trial. Radiology 2019; 291:205-211. [PMID: 30667335 DOI: 10.1148/radiol.2018181050] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To compare the Vancouver risk calculator (VRC) with American College of Radiology (ACR) Lung Imaging Reporting and Data System (Lung-RADS) in predicting the risk of malignancy in the National Lung Screening Trial (NLST). Materials and Methods A total of 2813 patients with 4408 nodules (4078 solid, 330 subsolid) were available from the NLST for evaluation. Nodules were scored by using VRC with nine parameters (output was the percentage likelihood of malignancy; VRC threshold for malignancy likelihood set as greater than 5%) and Lung-RADS (output was category 2-4B; malignancy defined as category 4A or 4B; malignancy likelihood greater than 5%). Lung-RADS and VRC were compared for sensitivity, specificity, and accuracy for malignancy on a per-nodule and per-patient basis. Results Of 4408 total nodules, 100 of 4078 (2.5%) solid nodules were malignant and 10 of 330 (3%) subsolid nodules were malignant. On an overall per-nodule basis, the sensitivity, specificity, and accuracy for VRC and Lung-RADS were 93%, 90%, and 90% for VRC and 87%, 83%, and 83% for Lung-RADS, respectively (P = .077, P < .001, and P < .001, respectively). On a per-patient basis, the sensitivity, specificity, and accuracy for VRC and Lung-RADS were 93%, 85%, and 85% for VRC and 87%, 76%, and 76% for Lung-RADS, respectively (P = .077, P < .001, and P < .001, respectively). Conclusion The Vancouver risk calculator had superior overall accuracy than the Lung Imaging Reporting and Data System in predicting malignancy in the National Lung Screening Trial for total nodules, as well as on a per-patient basis. © RSNA, 2019 See also the editorial by Black in this issue.
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Affiliation(s)
- Charles S White
- From the Department of Diagnostic Radiology, University of Maryland, 22 S Greene St, Baltimore, Md 21136 (C.S.W., R.C.); Philips Healthcare, Highland Heights, Ohio (E.D.); Philips Research North America, Cambridge, Mass (S.D.); and Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY (L.B.H.)
| | - Ekta Dharaiya
- From the Department of Diagnostic Radiology, University of Maryland, 22 S Greene St, Baltimore, Md 21136 (C.S.W., R.C.); Philips Healthcare, Highland Heights, Ohio (E.D.); Philips Research North America, Cambridge, Mass (S.D.); and Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY (L.B.H.)
| | - Sandeep Dalal
- From the Department of Diagnostic Radiology, University of Maryland, 22 S Greene St, Baltimore, Md 21136 (C.S.W., R.C.); Philips Healthcare, Highland Heights, Ohio (E.D.); Philips Research North America, Cambridge, Mass (S.D.); and Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY (L.B.H.)
| | - Rong Chen
- From the Department of Diagnostic Radiology, University of Maryland, 22 S Greene St, Baltimore, Md 21136 (C.S.W., R.C.); Philips Healthcare, Highland Heights, Ohio (E.D.); Philips Research North America, Cambridge, Mass (S.D.); and Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY (L.B.H.)
| | - Linda B Haramati
- From the Department of Diagnostic Radiology, University of Maryland, 22 S Greene St, Baltimore, Md 21136 (C.S.W., R.C.); Philips Healthcare, Highland Heights, Ohio (E.D.); Philips Research North America, Cambridge, Mass (S.D.); and Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY (L.B.H.)
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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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18
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Gariani J, Martin SP, Hachulla AL, Karenovics W, Adler D, Soccal PM, Becker CD, Montet X. Noninvasive pulmonary nodule characterization using transcutaneous bioconductance: Preliminary results of an observational study. Medicine (Baltimore) 2018; 97:e11924. [PMID: 30142805 PMCID: PMC6113006 DOI: 10.1097/md.0000000000011924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
We sought to assess the use of an electro pulmonary nodule (EPN) scanner (FreshMedx, Salt Lake City, UT) in the noninvasive characterization of pulmonary nodules using transcutaneous bioconductance.Monocentric prospective study including patients with a pulmonary nodule identified on a chest computed tomography scan. Study protocol approved by the institutional review board and written consent was obtained for every patient. 32 patients (12 females and 20 males), average age 65 years, and average lesion size 33.1 mm (range: 9-123 mm). Data collection by a trained physician, 62 skin surface measurements on the chest, arms, and hands bilaterally. Results were anonymized and mailed to a central data center for analysis and compared to histopathology.Pathology results obtained by percutaneous biopsy (n = 14), surgical biopsy (n = 1), or surgical resection (n = 17) showed 29 malignant lesions (adenocarcinoma n = 21, squamous cell carcinoma n = 5, typical carcinoid n = 1, metastasis n = 2), and 3 benign lesions (necrotic granuloma n = 1, no malignant cells on biopsy n = 2). EPN scanner results had a specificity of 66.67% (95% confidence interval [CI] 0.09-0.99), sensitivity 72.41% (95% CI 0.53-0.87), positive predictive value 95.45% (95% CI 0.81-0.99), and a negative predictive value 20.00% (95% CI 0.08-0.40).This pilot study showed a high positive predictive value of the EPN scanner, allowing aggressive management of lung nodules characterized as malignant. The low negative predictive value warrants further investigation of nodules that are characterized as benign.
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Affiliation(s)
| | | | | | | | - Dan Adler
- Division of Pneumology, Geneva University Hospitals, Geneva, Switzerland
| | - Paola M. Soccal
- Division of Pneumology, Geneva University Hospitals, Geneva, Switzerland
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