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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

131
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...
131

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Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma.

Thomas Lévi-Strauss1, Bettina Tortorici2, Olivier Lopez2

  • 1Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France.

Diagnostics (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

Radiomics, using artificial intelligence on medical images, offers a non-invasive way to analyze tumors for better cancer diagnosis and treatment guidance. While promising for precision medicine, its accuracy can be improved with additional data.

Keywords:
artificial intelligencehepatocellular carcinomaprecision medicineradiomics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Radiomics analyzes quantitative data from medical images using artificial intelligence.
  • It offers a non-invasive, cost-effective method for detailed patient and tumor characterization.
  • Radiomics is a promising biomarker for precision medicine, aiding diagnosis and treatment guidance.

Purpose of the Study:

  • To explore the strengths and weaknesses of radiomics in oncology.
  • To demonstrate radiomics' potential in diagnosing and managing hepatocellular carcinoma.
  • To assess radiomics' ability to predict histological features and treatment response.

Main Methods:

  • Quantitative and high-throughput analysis of image textural richness.
  • Application of artificial intelligence algorithms to medical image data.
  • Case study focusing on hepatocellular carcinoma to illustrate diagnostic and prognostic capabilities.

Main Results:

  • Radiomics enables comprehensive tumor analysis, crucial for understanding heterogeneity and prognosis.
  • The technology shows potential in facilitating diagnosis, predicting histology, and estimating treatment response in hepatocellular carcinoma.
  • Current radiomics models sometimes require integration with non-radiomics data for improved accuracy.

Conclusions:

  • Radiomics is a valuable, non-invasive tool in oncology, particularly for understanding tumor heterogeneity.
  • Its application in hepatocellular carcinoma can aid in diagnosis and treatment strategy.
  • Further development and data integration are needed to maximize radiomics' clinical performance.