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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.
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Imaging Studies III: Computed Tomography01:27

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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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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
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Imaging Studies I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Related Experiment Video

Updated: Feb 24, 2026

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models
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Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports.

David Le1, Ramon Correa-Medero1, Amara Tariq1

  • 1Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 23, 2026
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Summary

Deep learning models integrating CT scans and radiology reports can predict pancreatic cancer (PDAC) risk. This approach shows promise for early detection and improved survival outcomes in patients with this aggressive disease.

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with poor prognosis.
  • Late-stage diagnosis is common, necessitating improved early detection and risk stratification methods.

Purpose of the Study:

  • To develop and assess deep learning fusion models for predicting PDAC risk.
  • To integrate radiology reports and CT imaging data for enhanced prognostic accuracy.

Main Methods:

  • Implementation and evaluation of deep learning fusion models, specifically DeepSurv.
  • Utilizing a combined dataset of radiology reports and CT imaging for survival risk estimation.
  • Validation on both internal and external datasets.

Main Results:

  • The DeepSurv model achieved a concordance index (C-index) of 0.6773 (internal) and 0.6596 (external) for 5-year survival risk.
  • Kaplan-Meier analysis showed significant differentiation (p<0.0001) between predicted low and high-risk groups.
  • Demonstrated the model's ability to effectively stratify patients based on survival risk.

Conclusions:

  • Deep learning survival models integrating clinical and imaging data show significant potential for PDAC risk prediction.
  • These models can aid in early detection and personalized treatment strategies for pancreatic cancer.
  • Further research can refine these AI-driven approaches for improved patient outcomes.