Yun Li1, Jun Wang. 1. Department of Thoracic Surgery, People’s Hospital of Peking University, No. 11 Xizhimen South Street, Xicheng District, 100044 Beijing, People’s Republic of China. xiongwai@263.net
Abstract
BACKGROUND: The goal of the present study was to differentiate between benign and malignant solitary pulmonary nodules (SPN) by developing a mathematical prediction model. METHODS: Records from 371 patients (197 male, 174 female) with SPN between January 2000 and September 2009 were reviewed (group A). Clinical data were collected to estimate the independent predictors of malignancy of SPN with multivariate logistic regression analysis. A clinical prediction model was subsequently developed. Between October 2009 and May 2011, data from an additional 145 patients with SPN were used to validate this new clinical prediction model (group B). The same data were also estimated with two previously published models for comparison with our new model. RESULTS: The median patient age was 57.1 years in group A; 54% of the nodules were malignant and 46% were benign. Logistic regression analysis identified six clinical characteristics (age, diameter, border, calcification, spiculation, and family history of tumor) as independent predictors of malignancy in patients with SPN. The area under the receiver operator characteristic (ROC) curve for our model (0.874 ± 0.028) was higher than those generated using the other two reported models. In our model, sensitivity = 94.5%, specificity = 70.0%, positive predictive value = 87.8%, and negative predictive value = 84.8%). CONCLUSIONS: Age, diameter, border, calcification, spiculation, and family history of tumor were independent predictors of malignancy in patients with SPN. Our prediction model was sufficient to estimate malignancy in patients with SPN and proved to be more accurate than the two existing models.
BACKGROUND: The goal of the present study was to differentiate between benign and malignant solitary pulmonary nodules (SPN) by developing a mathematical prediction model. METHODS: Records from 371 patients (197 male, 174 female) with SPN between January 2000 and September 2009 were reviewed (group A). Clinical data were collected to estimate the independent predictors of malignancy of SPN with multivariate logistic regression analysis. A clinical prediction model was subsequently developed. Between October 2009 and May 2011, data from an additional 145 patients with SPN were used to validate this new clinical prediction model (group B). The same data were also estimated with two previously published models for comparison with our new model. RESULTS: The median patient age was 57.1 years in group A; 54% of the nodules were malignant and 46% were benign. Logistic regression analysis identified six clinical characteristics (age, diameter, border, calcification, spiculation, and family history of tumor) as independent predictors of malignancy in patients with SPN. The area under the receiver operator characteristic (ROC) curve for our model (0.874 ± 0.028) was higher than those generated using the other two reported models. In our model, sensitivity = 94.5%, specificity = 70.0%, positive predictive value = 87.8%, and negative predictive value = 84.8%). CONCLUSIONS: Age, diameter, border, calcification, spiculation, and family history of tumor were independent predictors of malignancy in patients with SPN. Our prediction model was sufficient to estimate malignancy in patients with SPN and proved to be more accurate than the two existing models.
Authors: K Nakamura; H Yoshida; R Engelmann; H MacMahon; S Katsuragawa; T Ishida; K Ashizawa; K Doi Journal: Radiology Date: 2000-03 Impact factor: 11.105
Authors: S J Swensen; M D Silverstein; E S Edell; V F Trastek; G L Aughenbaugh; D M Ilstrup; C D Schleck Journal: Mayo Clin Proc Date: 1999-04 Impact factor: 7.616
Authors: Heber MacMahon; John H M Austin; Gordon Gamsu; Christian J Herold; James R Jett; David P Naidich; Edward F Patz; Stephen J Swensen Journal: Radiology Date: 2005-11 Impact factor: 11.105
Authors: Carlos M Mery; Anastasia N Pappas; Raphael Bueno; Steven J Mentzer; Jeanne M Lukanich; David J Sugarbaker; Michael T Jaklitsch Journal: Chest Date: 2004-06 Impact factor: 9.410
Authors: Samantha K N Dilger; Johanna Uthoff; Alexandra Judisch; Emily Hammond; Sarah L Mott; Brian J Smith; John D Newell; Eric A Hoffman; Jessica C Sieren Journal: J Med Imaging (Bellingham) Date: 2015-09-01
Authors: Johanna Uthoff; Nicholas Koehn; Jared Larson; Samantha K N Dilger; Emily Hammond; Ann Schwartz; Brian Mullan; Rolando Sanchez; Richard M Hoffman; Jessica C Sieren Journal: Eur Radiol Date: 2019-04-01 Impact factor: 5.315
Authors: Ober van Gómez López; Ana María García Vicente; Antonio Francisco Honguero Martínez; Germán Andrés Jiménez Londoño; Carlos Hugo Vega Caicedo; Pablo León Atance; Ángel María Soriano Castrejón Journal: Transl Lung Cancer Res Date: 2015-06
Authors: Samuel G Armato; Karen Drukker; Feng Li; Lubomir Hadjiiski; Georgia D Tourassi; Roger M Engelmann; Maryellen L Giger; George Redmond; Keyvan Farahani; Justin S Kirby; Laurence P Clarke Journal: J Med Imaging (Bellingham) Date: 2016-12-19
Authors: Johanna M Uthoff; Sarah L Mott; Jared Larson; Christine M Neslund-Dudas; Ann G Schwartz; Jessica C Sieren Journal: Chronic Obstr Pulm Dis Date: 2022-04-29
Authors: Mathilde M Winkler Wille; Sarah J van Riel; Zaigham Saghir; Asger Dirksen; Jesper Holst Pedersen; Colin Jacobs; Laura Hohwü Thomsen; Ernst Th Scholten; Lene T Skovgaard; Bram van Ginneken Journal: Eur Radiol Date: 2015-03-13 Impact factor: 5.315