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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

893
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...
893

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Related Experiment Video

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Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound
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Multimodal Imaging of Osteosarcoma: From First Diagnosis to Radiomics.

Maurizio Cè1, Michaela Cellina2, Thirapapha Ueanukul3

  • 1Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy.

Cancers
|February 26, 2025
PubMed
Summary

Imaging is vital for diagnosing and managing osteosarcoma, a primary bone cancer. This review details various imaging techniques, including conventional radiography and advanced MRI, to aid radiologists in identifying subtypes and improving patient care.

Keywords:
Bone-RADSartificial intelligencebone cancermultimodal imagingosteosarcomaprimary malignant bone tumor

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Osteosarcoma is a primary malignant bone tumor producing osteoid matrix.
  • Histology is definitive, but imaging is crucial for diagnosis, planning, and follow-up.
  • Early osteosarcoma symptoms are often vague, posing diagnostic challenges.

Purpose of the Study:

  • To review the role of various imaging modalities in osteosarcoma diagnosis and management.
  • To illustrate typical and atypical imaging presentations of osteosarcoma subtypes.
  • To discuss advancements in artificial intelligence for osteosarcoma imaging.

Main Methods:

  • Conventional radiography as the initial imaging checkpoint.
  • Computed Tomography (CT) for bone matrix evaluation.
  • Magnetic Resonance Imaging (MRI) for soft tissue and medullary canal assessment.
  • Bone scans and PET/CT for detecting metastases.
  • Advanced MRI techniques (DCE-MRI, DWI, perfusion MRI) for tumor characterization and treatment response.
  • Review of a hospital case series with representative imaging examples.

Main Results:

  • Conventional radiography initiates assessment; Bone Reporting and Data System (Bone-RADS) stratifies risk.
  • CT evaluates bone matrix; bone scans/PET/CT detect metastases.
  • MRI assesses lesion extent, neurovascular involvement, and skip lesions.
  • Advanced MRI techniques characterize tumor microenvironment and treatment response.
  • Different osteosarcoma subtypes have distinct clinical and imaging features.

Conclusions:

  • Integrated imaging approaches enable personalized osteosarcoma diagnosis and management.
  • Radiologists benefit from understanding subtype-specific imaging characteristics for differential diagnosis.
  • Artificial intelligence holds promise for future advancements in osteosarcoma imaging.