Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image.
Eur J Radiol
; 74(1): 124-9, 2010 Apr.
Article
en En
| MEDLINE
| ID: mdl-19261415
ABSTRACT
PURPOSE:
To introduce multilevel binomial logistic prediction model-based computer-aided diagnostic (CAD) method of small solitary pulmonary nodules (SPNs) diagnosis by combining patient and image characteristics by textural features of CT image. MATERIALS ANDMETHODS:
Describe fourteen gray level co-occurrence matrix textural features obtained from 2171 benign and malignant small solitary pulmonary nodules, which belongs to 185 patients. Multilevel binomial logistic model is applied to gain these initial insights.RESULTS:
Five texture features, including Inertia, Entropy, Correlation, Difference-mean, Sum-Entropy, and age of patients own aggregating character on patient-level, which are statistically different (P<0.05) between benign and malignant small solitary pulmonary nodules.CONCLUSION:
Some gray level co-occurrence matrix textural features are efficiently descriptive features of CT image of small solitary pulmonary nodules, which can profit diagnosis of earlier period lung cancer if combined patient-level characteristics to some extent.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
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Nódulo Pulmonar Solitario
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Neoplasias Pulmonares
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Año:
2010
Tipo del documento:
Article