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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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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
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...
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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
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Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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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...
941

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Related Experiment Video

Updated: Dec 24, 2025

Y-90 Radioembolization and PD-1 Inhibitor as Neoadjuvant Treatment in Hepatocellular Carcinoma
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Radiomics for liver tumours.

Constantin Dreher1, Philipp Linde2, Judit Boda-Heggemann3

  • 1Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany.

Strahlentherapie Und Onkologie : Organ Der Deutschen Rontgengesellschaft ... [Et Al]
|April 17, 2020
PubMed
Summary
This summary is machine-generated.

Radiomics, integrating quantitative imaging data, offers advanced analysis beyond visual assessment for personalized cancer care. This approach enhances diagnosis, treatment planning, and follow-up for oncologic patients, particularly in liver cancer radiotherapy.

Keywords:
Artificial intelligenceBig dataComputed tomographyMagnetic resonance imagingStereotactic body radiation therapy

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Area of Science:

  • Oncology
  • Radiology
  • Radiation Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Current oncology research emphasizes integrating quantitative, multiparametric, and functional imaging data.
  • Radiomics enables sophisticated analysis of imaging data, surpassing qualitative tissue change evaluation.
  • Quantitative imaging data supports tailored, tumor-specific diagnostics and individualized treatment for cancer patients.

Purpose of the Study:

  • To review and critically discuss the applications of radiomics in oncology.
  • To summarize current knowledge on the interdisciplinary integration of radiomics in oncologic patient care.
  • To focus on radiomics in liver cancer and oligometastases radiotherapy, incorporating multiparametric data into the workflow.

Main Methods:

  • Review of current research on radiomics integration in oncologic workflows.
  • Focus on quantitative, multiparametric, and functional imaging data analysis.
  • Integration of artificial intelligence techniques for enhanced treatment planning and follow-up.

Main Results:

  • Radiomics allows for sophisticated analysis of imaging data, moving beyond qualitative assessment.
  • Quantitative imaging data facilitates personalized diagnostic work-up and treatment strategies.
  • Integration of radiomics and AI can improve treatment planning, outcome prediction, and progression detection.

Conclusions:

  • Radiomics holds significant potential for advancing oncologic patient care through sophisticated data analysis.
  • The integration of radiomics into radiotherapy, especially for liver cancer, promises more individualized and effective treatment.
  • Future applications include improved disease diagnosis, treatment planning, delivery, and patient follow-up with outcome prediction.