AIMC Topic: Adrenal Gland Neoplasms

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CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.

Cardiovascular and interventional radiology
INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool...

Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

Clinical radiology
AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.

Surgical Approaches to the Adrenal Gland.

The Surgical clinics of North America
Adrenalectomy can be performed open, endoscopically or robotically, utilizing a transabdominal or retroperitoneal approach. This chapter describes the relevant anatomy, various approaches and surgical techniques, pre-operative work-up and optimizatio...

Insulin resistance and adrenal incidentalomas: A bidirectional relationship.

Maturitas
An adrenal incidentaloma (AI) is an adrenal mass incidentally found via a radiological modality, independent of an endocrinological investigation. In this review, we aimed to investigate the possible reasons behind the increased frequency in AI detec...

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal ...

Cardiovascular events in patients with mild autonomous cortisol secretion: analysis with artificial neural networks.

European journal of endocrinology
BACKGROUND: The independent role of mild autonomous cortisol secretion (ACS) in influencing the cardiovascular event (CVE) occurrence is a topic of interest. We investigated the role of mild ACS in the CVE occurrence in patients with adrenal incident...

ASO Author Reflections: Clinical-Radiomic Machine Learning Model Predicts Pheochromocytomas and Paragangliomas Surgical Difficulty: A Retrospective Study.

Annals of surgical oncology
This study developed a machine learning (ML) model combining clinical and radiomic features to predict surgical difficulty in pheochromocytomas and paragangliomas (PPGLs), aiming to optimize preoperative planning and reduce perioperative complication...

Machine Learning Model for Predicting Pheochromocytomas/Paragangliomas Surgery Difficulty: A Retrospective Cohort Study.

Annals of surgical oncology
OBJECTIVE: We aimed to develop a machine learning (ML) model to preoperatively predict surgical difficulty for pheochromocytomas and paragangliomas (PPGLs) using clinical and radiomic features.

Identification of hypertension subtypes using microRNA profiles and machine learning.

European journal of endocrinology
OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are classified as "primary hypertension" (PHT), endocrine hypertension (EHT) accounts for ∼10% of cases an...

Two-Stage Deep Learning Model for Adrenal Nodule Detection on CT Images: A Retrospective Study.

Radiology
Background The detection and classification of adrenal nodules are crucial for their management. Purpose To develop and test a deep learning model to automatically depict adrenal nodules on abdominal CT images and to simulate triaging performance in ...