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Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

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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.
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Radiomics in Soft Tissue Sarcoma: Toward Precision Imaging in Oncology.

Anuj Shah1, Francesco Alessandrino1,2, Emanuela Palmerini2,3

  • 1Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.

Cancers
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

Radiomics, a quantitative imaging analysis, aids in soft tissue sarcoma characterization using CT and MRI. This data-driven approach enhances diagnosis, grading, and treatment response, driving precision oncology.

Keywords:
MRIbiomarkerneoplasmradiologyradiomicssoft tissue sarcomatreatment response

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Area of Science:

  • Oncology
  • Radiology
  • Data Science

Background:

  • Soft tissue sarcomas require accurate characterization for effective treatment.
  • Traditional imaging methods have limitations in fully assessing tumor heterogeneity and behavior.
  • Quantitative imaging biomarkers are needed to improve diagnostic accuracy and prognostic prediction.

Purpose of the Study:

  • To explore the role of radiomics in characterizing soft tissue sarcomas.
  • To evaluate the potential of radiomic features for improving diagnosis, grading, and treatment response assessment.
  • To investigate the integration of radiomics with machine learning for enhanced predictive modeling.

Main Methods:

  • Extraction of quantitative features (size, shape, heterogeneity) from CT and MRI scans.
  • Development of radiomics models, often integrated with clinical variables.
  • Application of machine learning for feature selection and predictive modeling.

Main Results:

  • Radiomics shows promise in distinguishing benign from malignant lesions and subtyping sarcomas.
  • Integrated radiomics and clinical data models achieve performance comparable to expert radiologists.
  • Machine learning enhances the automation and predictive power of radiomics analysis.

Conclusions:

  • Radiomics offers a data-driven approach to improve diagnostic accuracy and prognostic capabilities in soft tissue sarcoma.
  • Standardization and multi-institutional collaboration are crucial for clinical integration and validation.
  • Radiomics can serve as novel quantitative imaging biomarkers, advancing precision oncology for sarcomas.