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      Predicting the Risk of Malignancy of Lung Nodules Diagnosed as Indeterminate on Radial Endobronchial Ultrasound-Guided Biopsy

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          Abstract

          The next diagnostic step in cases of indeterminate radial probe endobronchial ultrasound (radial EBUS)-guided biopsy results remains uncertain. This study aimed to identify risk factors for malignancy based on clinical findings, chest computed tomography (CT), and radial EBUS images, and to estimate the risk of malignancy in lung nodules that showed indeterminate radial EBUS-guided biopsy results by constructing a nomogram. This retrospective study included 157 patients with indeterminate results on an initial radial EBUS biopsy performed at the Samsung Medical Center from January 2017 to December 2018, but with a definitive final diagnosis. Medical records, chest CT, radial EBUS images, and the final diagnoses were reviewed. Patients were randomly divided into training and validation sets. Factors related to malignancy were identified through logistic regression analysis, and a nomogram was constructed using the training set and subsequently applied to the validation set. Six factors in univariable and multivariable analyses, including upper lobe location, spiculation, satellite nodules, echogenicity, presence of dots or linear arcs, and patency of vessels and bronchi predicted malignancy. A nomogram was constructed based on these predictors. The area under the curve (AUC) value of the nomogram was 0.858 using the chest CT factors, which improved to 0.952 when radial EBUS factors were added. The calibration curve showed good agreement between the actual and nomogram-predicted malignancy outcomes. The utility of radial EBUS images for revealing risk factors of malignancy was confirmed. Furthermore, our nomogram was able to predict the probability of malignancy in lung nodules with indeterminate radial EBUS-guided biopsy results.

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          Most cited references26

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          How to build and interpret a nomogram for cancer prognosis.

          Nomograms are widely used for cancer prognosis, primarily because of their ability to reduce statistical predictive models into a single numerical estimate of the probability of an event, such as death or recurrence, that is tailored to the profile of an individual patient. User-friendly graphical interfaces for generating these estimates facilitate the use of nomograms during clinical encounters to inform clinical decision making. However, the statistical underpinnings of these models require careful scrutiny, and the degree of uncertainty surrounding the point estimates requires attention. This guide provides a nonstatistical audience with a methodological approach for building, interpreting, and using nomograms to estimate cancer prognosis or other health outcomes.
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            Probability of cancer in pulmonary nodules detected on first screening CT.

            Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up. We analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer. In the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set. Predictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.).
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              British Thoracic Society guidelines for the investigation and management of pulmonary nodules.

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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                13 November 2020
                November 2020
                : 9
                : 11
                : 3652
                Affiliations
                [1 ]Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; aboutsweets@ 123456gmail.com (S.Z.); myacousticlung@ 123456gmail.com (B.-H.J.); sangwonum@ 123456skku.edu (S.-W.U.); hjk3425@ 123456skku.edu (H.K.); ojkwon@ 123456skku.edu (O.J.K.)
                [2 ]Statistics and Data Center, Samsung Medical Center, Seoul 06351, Korea; wsy.woo@ 123456samsung.com (S.-y.W.); seonwoo.kim@ 123456samsung.com (S.K.)
                [3 ]Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; jungeun34.lee@ 123456samsung.com
                [4 ]Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
                Author notes
                Author information
                https://orcid.org/0000-0002-3124-1718
                https://orcid.org/0000-0001-9207-0433
                https://orcid.org/0000-0001-9960-5648
                https://orcid.org/0000-0002-1642-8111
                Article
                jcm-09-03652
                10.3390/jcm9113652
                7697809
                33202883
                ce8755da-d230-4980-9e8a-63dabdebae28
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 09 October 2020
                : 11 November 2020
                Categories
                Article

                radial ebus,lung nodule,nomogram
                radial ebus, lung nodule, nomogram

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