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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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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Imaging Studies VII: Vascular Imaging01:19

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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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Imaging Studies IV: Magnetic Resonance Imaging01:27

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study.

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  • 1University of California, San Francisco, San Francisco, CA.

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Summary
This summary is machine-generated.

Retrospective harmonization of heterogeneous research data is time-consuming. This study highlights challenges and proposes solutions for improving data interoperability and reuse in repositories like The Cancer Imaging Archive (TCIA).

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Cancer Research

Background:

  • Data sharing enhances research reproducibility and cost-efficiency but is hindered by the need for retrospective harmonization of heterogeneous data.
  • Existing interoperability efforts often focus on standards but neglect challenges posed by competing standards and the value of legacy data.
  • Retrospective harmonization remains a significant bottleneck for researchers aiming to leverage existing datasets.

Purpose of the Study:

  • To investigate the challenges and quantify the burden of retrospective harmonization for non-image data in a large cancer imaging repository.
  • To explore methods for improving cross-study data query and aggregation by harmonizing diverse data sources.
  • To develop recommendations and tools to reduce the effort required for retrospective data harmonization.

Main Methods:

  • Utilized 9 non-image files from The Cancer Imaging Archive (TCIA) lung and brain datasets, encompassing 659 fields.
  • Assessed the time and effort required to identify overlapping fields and transform data for harmonization, targeting Genomic Data Commons (GDC) standards.
  • Developed a web tool to facilitate exploration of harmonized data collections.

Main Results:

  • Identifying and transforming overlapping data fields for cross-study analysis proved to be a labor-intensive process, requiring 329.5 hours over six months.
  • Successfully harmonized 31 fields from 41 identified overlapping fields across multiple files.
  • A functional web tool was created to enable easier access and exploration of the harmonized data.

Conclusions:

  • Retrospective harmonization of heterogeneous data presents substantial challenges, even when using established standards like Digital Imaging and Communications in Medicine (DICOM).
  • The findings underscore the need for national infrastructure and improved strategies to mitigate the burden of retrospective harmonization.
  • Prospective adoption of standards is beneficial, but addressing retrospective harmonization issues is critical for maximizing the value of existing research data.