Predicting lung nodules malignancy.
Diagnosis
Malignant tumour
Prediction model
lung cancer
lung nodule
Journal
Pulmonology
ISSN: 2531-0437
Titre abrégé: Pulmonology
Pays: Spain
ID NLM: 101723786
Informations de publication
Date de publication:
Historique:
received:
24
02
2020
revised:
26
06
2020
accepted:
29
06
2020
pubmed:
3
8
2020
medline:
9
11
2022
entrez:
3
8
2020
Statut:
ppublish
Résumé
It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting. The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy. From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573. The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.
Sections du résumé
BACKGROUND
BACKGROUND
It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting.
METHODS
METHODS
The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy.
RESULTS
RESULTS
From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573.
CONCLUSIONS
CONCLUSIONS
The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.
Identifiants
pubmed: 32739327
pii: S2531-0437(20)30148-3
doi: 10.1016/j.pulmoe.2020.06.011
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
454-460Informations de copyright
Copyright © 2020 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved.