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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Imaging Studies for Cardiovascular System V: CT01:28

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Anatomy of the Adrenal Glands01:17

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The adrenal or supra-renal glands, situated above the kidneys and aligned with the twelfth rib, are paired pyramid-shaped structures crucial for the body's stress response. During stress, these glands secrete hormones vital for adaptive physiological reactions.
These glands possess a distinctive yellow tinge due to the stored cholesterol and fatty acids required for hormone synthesis. They are encased in a fibrous capsule and cushioned by fat.
The adrenal gland comprises two distinct...
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Related Experiment Video

Updated: Jul 25, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Deep learning approach for differentiating indeterminate adrenal masses using CT imaging.

Yashbir Singh1, Zachary S Kelm1, Shahriar Faghani1

  • 1Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Abdominal Radiology (New York)
|June 27, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning accurately distinguishes adrenocortical carcinoma (ACC) from large adrenal adenomas (LPAA) using CT scans. This AI approach shows promise for improving diagnostic accuracy in adrenal mass evaluation.

Keywords:
Adrenocortical carcinomaComputed tomographyDeep learningLipid poor adrenal adenoma

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Distinguishing stage 1-2 adrenocortical carcinoma (ACC) from large, lipid-poor adrenal adenomas (LPAA) is challenging due to overlapping imaging features on CT scans.
  • Accurate differentiation is crucial for appropriate patient management and treatment planning.

Purpose of the Study:

  • To investigate the efficacy of a deep learning model in differentiating between stage 1-2 ACC and LPAA using single time-point CT images.
  • To assess the diagnostic performance of a 3D Densenet121 model for this classification task.

Main Methods:

  • A retrospective cohort study included 48 patients with stage 1-2 ACC and 43 patients with LPAA (>3 cm).
  • Single time-point contrast-enhanced CT images were used as input for a 3D Densenet121 deep learning model.
  • Five-fold cross-validation was employed to evaluate model performance, reporting both accuracy-focused and sensitivity-focused checkpoints.

Main Results:

  • The sensitivity-focused deep learning model achieved a mean accuracy of 87.2% and 100% sensitivity.
  • The accuracy-focused model achieved a mean accuracy of 91% and 96% sensitivity.
  • The deep learning model demonstrated high performance in distinguishing ACC from LPAA.

Conclusions:

  • Deep learning models show significant potential for differentiating ACC from large LPAA on single-time point CT images.
  • Further multicentric and external validation are necessary before widespread clinical adoption of this AI tool.