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Related Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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 the...
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

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.
Fundamental Principles of PET

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Related Experiment Video

Updated: Jun 25, 2026

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
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Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.

Mark Ramaekers1, Christiaan G A Viviers2, Boris V Janssen3,4

  • 1Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.

Journal of Clinical Medicine
|July 14, 2023
PubMed
Summary

Artificial intelligence (AI) computer-aided detection (CAD) shows promise for improving pancreatic ductal adenocarcinoma (PDAC) detection in radiological imaging. However, challenges hinder its widespread clinical adoption, necessitating further solutions.

Keywords:
artificial intelligenceclinical implementationcomputer-aided detectiondiagnosticspancreatic ductal adenocarcinomaradiological imaging

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Radiological imaging is vital for pancreatic ductal adenocarcinoma (PDAC) detection and treatment.
  • Challenges in interpreting radiological images, especially post-neoadjuvant therapy, impact clinical practice.
  • Early tumor detection and characterization are crucial for curative treatment eligibility.

Purpose of the Study:

  • To review current AI-based computer-aided detection (CAD) systems for pancreatic cancer.
  • To highlight challenges in the clinical application of radiological CAD for PDAC.
  • To discuss potential solutions for overcoming these challenges and improving adoption.

Main Methods:

  • This study is a narrative review of existing literature on AI and CAD in pancreatic cancer.
  • It synthesizes information on current AI applications, clinical challenges, and proposed solutions.
  • Focuses on radiological imaging techniques and their integration with AI.

Main Results:

  • AI-based CAD systems offer promising advancements in detecting and assessing pancreatic cancer extent.
  • Significant challenges remain in the widespread clinical adoption of these AI tools.
  • Expertise is required for accurate interpretation, particularly after neoadjuvant therapy.

Conclusions:

  • AI-based CAD has the potential to enhance radiological detection of PDAC.
  • Addressing current implementation challenges is key to realizing the full clinical benefits of AI in pancreatic cancer care.
  • Further research and development are needed to facilitate broader clinical integration.