The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma.
BMC Cancer
; 20(1): 533, 2020 Jun 08.
Article
em En
| MEDLINE
| ID: mdl-32513144
ABSTRACT
BACKGROUND:
Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN.METHODS:
This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated.RESULTS:
The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI] 0.735-0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI 0.707-0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI 0.723-0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power.CONCLUSION:
The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Tomografia Computadorizada por Raios X
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Nomogramas
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Adenocarcinoma de Pulmão
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Neoplasias Pulmonares
Tipo de estudo:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article