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Modelling level I Axillary Lymph Nodes depth for Microwave Imaging.

Daniela M Godinho1, Carolina Silva2, Cláudia Baleia2

  • 1Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|December 4, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a mathematical model using Body Mass Index (BMI) to predict the depth of axillary lymph nodes (ALNs). This aids in developing advanced imaging technologies for breast cancer staging.

Keywords:
Axillary Lymph NodesBreast cancer stagingMagnetic Resonance ImagingMicrowave Imaging

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Oncology

Background:

  • Accurate axillary lymph node (ALN) depth is crucial for breast cancer staging and developing diagnostic imaging like Microwave Imaging (MWI).
  • Previous studies on ALN depth lacked sufficient sample size and data usability for inferring depth.
  • Predicting ALN depth is vital for treatment planning and improving imaging technologies.

Purpose of the Study:

  • To create a mathematical model for predicting the depth interval of level I axillary lymph nodes (ALNs).
  • To identify key biometric features for accurate ALN depth prediction.
  • To integrate the predictive model into Microwave Imaging (MWI) algorithms.

Main Methods:

  • Trained regression models using biometric features from 98 patients' breast Magnetic Resonance Imaging (MRI) scans.
  • Evaluated various feature combinations to determine the best predictor for ALN depth.
  • Implemented and tested the prediction models within an algorithm for MWI using anthropomorphic phantoms.

Main Results:

  • Body Mass Index (BMI) emerged as the most effective predictor of ALN depth.
  • The model achieved a coefficient of determination (R²) between 0.49 and 0.55.
  • The prediction model demonstrated satisfactory performance in MWI, validated across diverse BMIs.

Conclusions:

  • The developed mathematical model and identified predictors enhance the development of novel imaging technologies.
  • This research contributes to improved assessment of ALNs in breast cancer staging and other medical applications.
  • The findings support the advancement of reconstruction algorithms for emerging imaging modalities.