XGBoost Enhances the Performance of SAFE: A Novel Microwave Imaging System for Early Detection of Malignant Breast Cancer

Ali Yurtseven*, Aleksandar Janjic, Mehmet Cayoren, Onur Bugdayci, Mustafa Erkin Aribal, Ibrahim Akduman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background/Objectives: Breast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. Mammography is widely used but has limitations, particularly for younger women with denser breasts. These include reduced sensitivity, false positives, and radiation risks. This highlights the need for alternative screening methods. In this study, we assess the performance of SAFE (Scan and Find Early), a novel microwave imaging device, in detecting breast cancer in a larger patient cohort. Unlike previous studies that predominantly relied on cross-validation, this study employs a more reliable, independent evaluation methodology to enhance generalizability. Methods: We developed an XGBoost model to classify breast cancer cases into positive (malignant) and negative (benign or healthy) groups. The model was analyzed with respect to key factors such as breast size, density, age, tumor size, and histopathological findings. This approach provides a better understanding of how these factors influence the model’s performance, using an independent evaluation methodology for increased reliability. Results: Our results demonstrate that SAFE exhibits high sensitivity, particularly in dense breasts (91%) and younger patients (83%), suggesting its potential as a supplemental screening tool. Additionally, the system shows high detection accuracy for both small (<2 cm) and larger lesions, proving effective in early cancer detection. Conclusions: This study reinforces the potential of SAFE to complement existing screening methods, particularly for patients with dense breasts, where mammography’s sensitivity is reduced. The promising results warrant further research to solidify SAFE’s clinical application as an alternative screening tool for breast cancer detection.

Original languageEnglish
Article number214
JournalCancers
Volume17
Issue number2
DOIs
Publication statusPublished - Jan 2025

Bibliographical note

Publisher Copyright:
© 2025 by the authors.

Keywords

  • SAFE
  • breast cancer detection
  • machine learning
  • medical imaging
  • microwave imaging
  • tumor classification

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