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This study introduces a new breast cancer (BC) detection method using multi-wavelength interference (MWI) phase imaging. The technique accurately distinguishes cancerous from healthy tissue, aiding early diagnosis and treatment.

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Cancer Research

Background:

  • Breast cancer (BC) poses a significant global health challenge, underscoring the need for effective early detection methods.
  • Early diagnosis is paramount for improving treatment outcomes and patient survival rates in BC.

Purpose of the Study:

  • To develop and validate a novel breast cancer detection approach utilizing multi-wavelength interference (MWI) phase imaging combined with diffuse reflection hyperspectral (HS) imaging.
  • To assess the capability of the proposed imaging methodology in differentiating between cancerous and normal breast tissue based on refractive index variations.

Main Methods:

  • Employed multi-wavelength interference (MWI) phase imaging, measuring interference patterns between blue (446.6 nm) and red (632 nm) wavelengths.
  • Utilized Fast Fourier (FF) transform analysis for image processing and categorization, focusing on refractive index changes between tumor and normal tissues.
  • Developed and validated a classifier algorithm for hyperspectral (HS) cube data categorization of ex-vivo breast cancer tissue, incorporating signal normalization with the FF transform algorithm.

Main Results:

  • Observed significantly increased refractive index variance in tumors compared to normal tissues, indicating altered tissue organization due to cancer.
  • Achieved high performance in classifying 632 nm images with 94% specificity (Spec) and 90.9% sensitivity (Sen) for ex-vivo breast cancer tissue.
  • Successfully generated 3D phase-resolved images from unstained tissue, clearly highlighting differences in diffuse reflectance between cancerous and healthy areas.

Conclusions:

  • The developed MWI phase imaging technique shows promise for accurate breast cancer detection and characterization.
  • Preliminary findings suggest the potential for this optical technology to assist surgeons in real-time assessment during tumor resection and margin evaluation.
  • The methodology, utilizing a standard RGB camera with wavelength-specific operation, could lead to effective in-vivo disease description.