Adrenal imaging for adenoma characterization: imaging features, diagnostic accuracies and differential diagnoses

Park, Jung Jae; Park, Byung Kwan; Kim, Chan Kyo

doi:10.1259/bjr.20151018

Cited by 45 publications

(26 citation statements)

References 64 publications

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“…In contrast, the non-tumorous adrenal cortex is not atrophic in glands harboring an APA (24). These findings were basically consistent with previous radiological and pathological reports (7,24,25). Although it is still insufficient to distinguish the two tumors on conventional CT image, it may give radiomics the possibility to extract more correlated quantitative features for improving decision support.…”

Section: Discussionsupporting

confidence: 89%

“…The gold standard for the diagnosis of APA is through a technically difficult and invasive procedure that samples from a vein located near the adrenal glands, called adrenal vein sampling (AVS), to determine aldosterone and cortisol levels (4,5) The success rate of right AVS is as low as 10% because of the particular and complex anatomical structure (6). Moreover, about 10-20% of ACAs are bilateral or multiple (7,8). In such condition, it is very important, but also quite difficult, to distinguish the responsible foci to avoid unnecessary excision or overresection for performing precision treatment.…”

Section: Introductionmentioning

confidence: 99%

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A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram

et al. 2020

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Purpose: To explore the application value of multiparametric computed tomography (CT) radiomics in non-invasive differentiation between aldosterone-producing and cortisol-producing functional adrenocortical adenomas. Methods: This retrospective review analyzed 83 patients including 41 patients with aldosterone-producing adenoma and 42 patients with cortisol-producing adenoma. The quantitative radiomics features were extracted from the complete unenhanced, arterial, and venous phase CT images. A comparative study of several frequently used machine learning models (linear discriminant analysis, logistic regression, random forest, and support vector machine) combined with different feature selection methods was implemented in order to determine which was most advantageous for differential diagnosis using radiomics features. Then, the integrated model using the combination of radiomic signature and clinic-radiological features was built, and the associated calibration curve was also presented. The diagnostic performance of these models was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC). Result: In the radiomics-based machine learning model, logistic regression model with LASSO (least absolute shrinkage and selection operator) outperformed the other models, which yielded a sensitivity of 0.935, a specificity of 0.823, and an accuracy of 0.887 [AUC = 0.882, 95% confidence interval (CI) = 0.819-0.945]. Moreover, the nomogram representing the integrated model achieved good discrimination performances, which yielded a sensitivity of 0.915, a specificity of 0.928, and an accuracy of 0.922 (AUC = 0.902, 95% CI = 0.822-0.982), and it was better than that of the radiomics model alone. Conclusion: This study found that the combination of multiparametric radiomics signature and clinic-radiological features can non-invasively differentiate the subtypes of hormone-secreting functional adrenocortical adenomas, which may have good potential for facilitating the diagnosis and treatment in clinical practice.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram

et al. 2020

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“…Incidental adrenal masses are mostly benign, and most are diagnosed as adenomas [ 21 , 22 ]. Adrenal mass biopsy is performed when the CT features of an adrenal mass are not consistent with adenoma, because CT is the best imaging modality for diagnosing adenoma [ 23 - 26 ]. However, biochemical tests should also be performed prior to adrenal mass biopsy, because subclinical pheochromocytoma must be excluded [ 26 ].…”

Section: Us-guided Biopsymentioning

confidence: 99%

“…Adrenal mass biopsy is performed when the CT features of an adrenal mass are not consistent with adenoma, because CT is the best imaging modality for diagnosing adenoma [ 23 - 26 ]. However, biochemical tests should also be performed prior to adrenal mass biopsy, because subclinical pheochromocytoma must be excluded [ 26 ]. A high suspicion of adrenal diseases that usually require medical treatment, such as lymphoma, metastasis, unresectable or metastatic adrenocortical carcinoma, or cancer-mimicking adrenal tuberculosis, is also an indication for adrenal biopsy to avoid unnecessary surgery ( Fig.…”

Section: Us-guided Biopsymentioning

confidence: 99%

Ultrasound-guided genitourinary interventions: principles and techniques

Park

2017

Ultrasonography

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Ultrasound (US) is often used to guide various interventional procedures in the genitourinary (GU) tract because it can provide real-time imaging without any radiation hazard. Moreover, US can clearly visualize the pathway of an aspiration or biopsy needle to ensure the safety of the intervention. US guidance also helps clinicians to access lesions via the transabdominal, transhepatic, transvaginal, transrectal, and transperineal routes. Hence, US-guided procedures are useful for radiologists who wish to perform GU interventions. However, US-guided procedures and interventions are difficult for beginners because they involve a steep initial learning curve. The purpose of this review is to describe the basic principles and techniques of US-guided GU interventions.

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“…Adrenocortical adenoma is the most common adrenal tumor. Because the majority of adenomas are non-functioning, most of these lesions are detected incidentally on routine imaging performed for unrelated reasons(7).…”

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confidence: 99%

Resistant Hypertension: A Clinical Case

et al. 2018

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Introducción: la hipertensión secundaria corresponde al 15% de las causas de hipertensión arterial, y entre ellas, el hiperaldosteronismo primario presenta una incidencia variable de sobre 3 % en pacientes hipertensos. Tiene una prevalencia ligeramente mayor en mujeres, entre 30 y 60 años, y generalmente es unilateral. Presentación de caso: los autores describen el caso clínico de un paciente, seguido por una hipertensión resistente medicada con cuatro fármacos antihipertensivos para la estabilización de la tensión, con mantenimiento de las tensiones arteriales sistólicas superiores a 170 mmHg. Las alteraciones analíticas en el estudio etiológico de la hipertensión sugirieron hiperaldosteronismo y una lesión nodular en la glándula suprarrenal izquierda. El paciente fue sometido a cirugía y se realizó la escisión de la lesión con confirmación histológica del diagnóstico de adenoma corticomedular de la glándula suprarrenal. El paciente presentó una mejora en el perfil de tensión, con la necesidad de suspender dos de los cuatro fármacos antihipertensivos y reducir la dosis de los restantes. Discusión: un tumor de la corteza suprarrenal que produce la aldosterona es la principal causa de hiperaldosteronismo primario y siempre debe excluirse cuando se presenta hipertensión grave, difícil de controlar, ya que el tratamiento estándar es quirúrgico y conduce a una estabilización del patrón de tensión después de unos meses.

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Adrenal imaging for adenoma characterization: imaging features, diagnostic accuracies and differential diagnoses

Cited by 45 publications

References 64 publications

A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram

A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram

Ultrasound-guided genitourinary interventions: principles and techniques

Resistant Hypertension: A Clinical Case

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