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Related Experiment Video

Updated: Aug 31, 2025

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Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

Afshin Mohammadi1, Mohammad Mirza-Aghazadeh-Attari2, Fariborz Faeghi3

  • 1Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine
|August 24, 2022
PubMed
Summary
This summary is machine-generated.

This study shows that analyzing the tumor microenvironment (TME) using extended imaging contours and machine learning can accurately differentiate benign from malignant breast and lymph node lesions. This approach improves diagnostic potential in medical imaging.

Keywords:
artificial intelligencebreast neoplasmslymph nodesmachine-learningradiomicstumor microenvironment

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Area of Science:

  • Medical Imaging
  • Oncology
  • Machine Learning

Background:

  • The tumor microenvironment (TME) influences tumor progression and immune response.
  • Accurate differentiation of benign and malignant lesions is crucial for patient management.

Purpose of the Study:

  • To investigate the diagnostic potential of the TME in differentiating benign and malignant lesions.
  • To utilize image quantification and machine learning for improved diagnostic accuracy.

Main Methods:

  • 229 breast lesions and 220 cervical lymph nodes were analyzed.
  • Radiomics features were extracted from extended contours of lesions.
  • A support vector machine (SVM) classifier was developed using significant radiomics features.

Main Results:

  • Extended contours yielded superior radiomics features compared to standard radiologist contours.
  • The SVM model with extended contour features achieved high accuracy (AUC 0.887 for breast, 0.970 for lymph nodes).

Conclusions:

  • The tumor periphery and TME are vital for medical imaging diagnosis.
  • Considering the immediate tumor periphery in image quantification can enhance the differentiation of benign and malignant lesions.