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CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI.

Domiziana Santucci1,2, Eliodoro Faiella2, Michela Gravina3

  • 1Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome "Campus Bio-medico", Via Alvaro del Portillo, 21, 00128 Rome, Italy.

Cancers
|October 14, 2022
PubMed
Summary

Predicting breast cancer lymph node status is improved by including peritumoral tissue in dynamic contrast-enhanced MRI analysis using convolutional neural networks (CNNs). This deep learning approach enhances accuracy for breast cancer (BC) patients.

Keywords:
axillary lymph nodes status (ALNS)bounding boxbreast cancer (BC)convolutional neural network (CNN)deep learning (DL)

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Axillary lymph node status (ALNS) is a critical prognostic factor for breast cancer (BC).
  • Current ALNS evaluation relies on invasive procedures.
  • Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers insights into tumor characteristics.

Purpose of the Study:

  • To evaluate the impact of peritumoral tissue inclusion on lymph node status (LNS) prediction accuracy.
  • To compare different tumor bounding strategies within a deep learning framework.
  • To assess the performance of convolutional neural networks (CNNs) for LNS prediction using DCE-MRI.

Main Methods:

  • 155 malignant BC lesions underwent DCE-MRI analysis.
  • Six tumor bounding options were investigated to predict LNS using CNNs.
  • Three CNN architectures (SFB-NET, VB-NET, 2DS-NET) were trained and evaluated via 10-fold cross-validation.

Main Results:

  • The 2DS-NET achieved the highest accuracy (78.63%) and AUC (77.86%).
  • 2DS-NET demonstrated superior specificity.
  • VB-NET, using SVB and SIB bounding options, yielded the highest sensitivity.

Conclusions:

  • Selective inclusion of peritumoral tissue in DCE-MRI enhances LNS prediction accuracy.
  • CNNs provide a viable deep learning approach for non-invasive LNS assessment.
  • This method holds potential for improving breast cancer prognostication.