CT Texture Analysis for Preoperative Identification of Lymphoma from Other Types of Primary Small Bowel Malignancies.
Biomed Res Int
; 2021: 5519144, 2021.
Article
en En
| MEDLINE
| ID: mdl-33884262
OBJECTIVES: To explore the application of computed tomography (CT) texture analysis in differentiating lymphomas from other malignancies of the small bowel. METHODS: Arterial and venous CT images of 87 patients with small bowel malignancies were retrospectively analyzed. The subjective radiological features were evaluated by the two radiologists with a consensus agreement. The region of interest (ROI) was manually delineated along the edge of the lesion on the largest slice, and a total of 402 quantified features were extracted automatically from AK software. The inter- and intrareader reproducibility was evaluated to select highly reproductive features. The univariate analysis and minimum redundancy maximum relevance (mRMR) algorithm were applied to select the feature subsets with high correlation and low redundancy. The multivariate logistic regression analysis based on texture features and radiological features was employed to construct predictive models for identification of small bowel lymphoma. The diagnostic performance of multivariate models was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The clinical data (age, melena, and abdominal pain) and radiological features (location, shape, margin, dilated lumen, intussusception, enhancement level, adjacent peritoneum, and locoregional lymph node) differed significantly between the nonlymphoma group and lymphoma group (p < 0.05). The areas under the ROC curve of the clinical model, arterial texture model, and venous texture model were 0.93, 0.92, and 0.87, respectively. CONCLUSION: The arterial texture model showed a great diagnostic value and fitted performance in preoperatively discriminating lymphoma from nonlymphoma of the small bowel.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Tomografía Computarizada por Rayos X
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Neoplasias Intestinales
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Intestino Delgado
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Linfoma
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adolescent
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Adult
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Aged
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Aged80
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Child
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Child, preschool
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Biomed Res Int
Año:
2021
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos