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Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?
Ho, Lisa M; Samei, Ehsan; Mazurowski, Maciej A; Zheng, Yuese; Allen, Brian C; Nelson, Rendon C; Marin, Daniele.
Afiliação
  • Ho LM; 1 Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC 22710.
  • Samei E; 1 Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC 22710.
  • Mazurowski MA; 2 Department of Biomedical Engineering, Duke University, Durham, NC.
  • Zheng Y; 3 Department of Physics, Duke University, Durham, NC.
  • Allen BC; 4 Department of Electrical and Computer Engineering, Duke University, Durham, NC.
  • Nelson RC; 5 Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC.
  • Marin D; 1 Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC 22710.
AJR Am J Roentgenol ; 212(3): 554-561, 2019 03.
Article em En | MEDLINE | ID: mdl-30620676
ABSTRACT

OBJECTIVE:

The purpose of this study is to determine whether second-order texture analysis can be used to distinguish lipid-poor adenomas from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT (CECT), and chemical-shift MRI. MATERIALS AND

METHODS:

In this retrospective study, 23 adrenal nodules (15 lipid-poor adenomas and eight adrenal malignancies) in 20 patients (nine female patients and 11 male patients; mean age, 59 years [range, 15-80 years]) were assessed. All patients underwent unenhanced CT, CECT, and chemical-shift MRI. Twenty-one second-order texture features from the gray-level cooccurrence matrix and gray-level run-length matrix were calculated in 3D. The mean values for 21 texture features and four imaging features (lesion size, unenhanced CT attenuation, CECT attenuation, and signal intensity index) were compared using a t test. The diagnostic performance of texture analysis versus imaging features was also compared using AUC values. Multivariate logistic regression models to predict malignancy were constructed for texture analysis and imaging features.

RESULTS:

Lesion size, unenhanced CT attenuation, and the signal intensity index showed significant differences between benign and malignant adrenal nodules. No significant difference was seen for CECT attenuation. Eighteen of 21 CECT texture features and nine of 21 unenhanced CT texture features revealed significant differences between benign and malignant adrenal nodules. CECT texture features (mean AUC value, 0.80) performed better than CECT attenuation (mean AUC value, 0.60). Multivariate logistic regression models showed that CECT texture features, chemical-shift MRI texture features, and imaging features were predictive of malignancy.

CONCLUSION:

Texture analysis has a potential role in distinguishing benign from malignant adrenal nodules on CECT and may decrease the need for additional imaging studies in the workup of incidentally discovered adrenal nodules.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Tomografia Computadorizada por Raios X / Adenoma / Neoplasias das Glândulas Suprarrenais Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: AJR Am J Roentgenol Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Tomografia Computadorizada por Raios X / Adenoma / Neoplasias das Glândulas Suprarrenais Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: AJR Am J Roentgenol Ano de publicação: 2019 Tipo de documento: Article