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Real-Time Tissue Classification Using a Novel Optical Needle Probe for Biopsy.

Lukasz Surazynski1,2, Ville Hassinen1, Miika T Nieminen1,3

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|February 19, 2024
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This study introduces a smart needle probe using diffuse optical spectroscopy for real-time tissue classification during core needle biopsies. The probe achieved nearly 80% accuracy in identifying rat organs, improving biopsy guidance.

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Area of Science:

  • Biomedical Engineering
  • Optical Spectroscopy
  • Machine Learning

Background:

  • Core needle biopsy is crucial for cancer diagnosis, typically guided by ultrasound.
  • Ultrasound guidance requires significant user expertise and can be affected by patient movement and artifacts.
  • An optically enhanced probe could provide real-time tissue property information, aiding radiologists.

Purpose of the Study:

  • To develop and characterize a core needle biopsy probe enhanced with diffuse optical spectroscopy.
  • To evaluate the probe's ability for real-time tissue differentiation and organ identification.
  • To validate the probe's potential to inform interventional radiologists during biopsy procedures.

Main Methods:

  • A custom core needle probe integrated with diffuse optical spectroscopy was designed.
  • Optical spectra were collected from various rat tissues (blood, fat, heart, kidney, liver, lungs, muscle).
  • Machine learning classifiers (SVM, k-NN) were trained and evaluated using k-fold cross-validation for tissue classification.

Main Results:

  • The probe successfully collected optical spectra from diverse biological tissues.
  • Machine learning models demonstrated feasibility in differentiating tissue types based on optical signatures.
  • The best-performing model achieved nearly 80% accuracy in real-time automated classification of rat organs.

Conclusions:

  • The diffuse optical spectroscopy-enhanced needle probe shows promise for real-time tissue recognition.
  • This technology could enhance the precision and safety of core needle biopsies.
  • Further development may lead to improved interventional radiology guidance systems.