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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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Related Experiment Video

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Practical Trade-Offs in 3D Cancer Imaging.

Lucie Dequiedt1, Ashley L Kiemen1,2,3,4

  • 1Department of Chemical and Biomolecular Engineering, Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland.

Cancer Research
|December 15, 2025
PubMed
Summary

Three-dimensional (3D) imaging offers superior insights into complex tumor structures compared to traditional 2D methods. Integrating multi-omics and hybrid 2D/3D approaches enhances understanding of cancer heterogeneity.

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Area of Science:

  • Oncology
  • Biomedical Imaging
  • Computational Biology

Background:

  • Traditional 2D imaging methods have limitations in capturing complex tumor architectures.
  • Advancements in imaging technology are crucial for a comprehensive understanding of cancer.

Purpose of the Study:

  • To highlight technical considerations for adopting 3D imaging in cancer research.
  • To discuss the integration of multi-omics data and virtual staining.
  • To propose hybrid 2D/3D imaging strategies for studying tumor heterogeneity.

Main Methods:

  • Review of technical considerations for 3D imaging adoption.
  • Discussion on multi-omics integration and virtual staining workflows.
  • Proposal of hybrid 2D and 3D imaging strategies.

Main Results:

  • 3D imaging captures complex and heterogeneous tumor architecture more effectively than 2D.
  • Multi-omics integration and virtual staining address scalability challenges.
  • Hybrid strategies offer a balanced approach to understanding inter- and intra-tumoral heterogeneity.

Conclusions:

  • 3D imaging provides unique advantages for cancer research.
  • Integrating multi-omics and virtual staining enhances 3D imaging utility.
  • Hybrid 2D/3D approaches are valuable for a comprehensive study of tumor heterogeneity.