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Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.

Joseph-Hang Leung1, Riya Karmakar2, Arvind Mukundan2

  • 1Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City 600566, Taiwan.

Bioengineering (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

Hyperspectral imaging (HSI) shows high accuracy in detecting breast cancer, outperforming traditional methods. This advanced technique offers improved sensitivity and specificity for early cancer diagnosis.

Keywords:
Deeks’ funnel chartbreast cancercomputer-aided detectiondiagnostic test accuracyforest chartshyperspectral imagingsystematic meta-analysis

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Area of Science:

  • Medical Imaging
  • Oncology
  • Biomedical Engineering

Background:

  • Breast cancer is a leading global cancer, often diagnosed via subjective pathologist interpretation.
  • Traditional computer-aided diagnosis (CAD) using RGB images has limitations in spectral analysis.
  • Hyperspectral imaging (HSI) offers advanced, non-destructive testing with a wide spectral band for improved image processing.

Purpose of the Study:

  • To systematically analyze the diagnostic test accuracy (DTA) of hyperspectral imaging (HSI) for breast cancer detection.
  • To compare HSI performance against traditional imaging methods and evaluate various HSI techniques.

Main Methods:

  • A systematic review and meta-analysis of eight diagnostic accuracy studies on HSI for breast cancer.
  • Studies were evaluated using the Quadas-2 tool and categorized by data ethnicity, methodology, wavelength, cancer type, and publication year.
  • Statistical analyses included Deeks' funnel charts and forest plots to assess heterogeneity and accuracy.

Main Results:

  • Meta-analysis of eight studies reported average HSI sensitivity of 78%, specificity of 89%, and accuracy of 87% for breast cancer detection.
  • Support Vector Machine (SVM) methods achieved the highest sensitivity (95%) and accuracy.
  • Convolutional Neural Network (CNN) methods were common but showed lower sensitivity (65.43%).
  • No significant heterogeneity was found among the studies, indicating consistent performance.

Conclusions:

  • HSI technology demonstrates high sensitivity, specificity, and accuracy for breast cancer diagnosis.
  • HSI is a promising non-destructive technique that surpasses traditional RGB imaging limitations.
  • The performance of HSI techniques for breast cancer detection has notably evolved, particularly post-2019.