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An Evaluation of Lymphedema Using Optical Coherence Tomography: A Rat Limb Model Approach
V V Nikolaev1, I A Trimassov1, D S Amirchanov1
1Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.
Diagnostics (Basel, Switzerland)
|September 9, 2023
Summary
Researchers developed a novel method to quantify lymphedema using optical coherence tomography (OCT) in a rat model. This technique accurately distinguishes between healthy and lymphedematous tissue, aiding in lymphedema assessment.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Dermatology
Background:
- Lymphedema, caused by impaired lymphatic flow, can lead to severe disability.
- Current methods for quantifying lymphedema lack precision and non-invasive capabilities.
- There is a need for advanced techniques to assess lymphedema development and progression.
Purpose of the Study:
- To develop and validate a non-invasive, in vivo method for assessing lymphedema.
- To utilize optical coherence tomography (OCT) for characterizing lymphedematous skin changes.
- To establish a machine learning algorithm for accurate lymphedema classification.
Main Methods:
- A small-animal model of lymphedema was created in rats via surgical lymph node resection and X-ray exposure.
- In vivo assessment of skin properties using optical coherence tomography (OCT).
- Histological examination of tissue biopsies to confirm lymphedema development.
- Development of a machine learning algorithm based on OCT signal intensity distribution.
Main Results:
- OCT revealed distinct differences in lymphedematous skin: thickened stratum corneum, thinned viable epidermis, and increased signal attenuation in the dermis.
- Histological analysis confirmed the presence of lymphedema.
- The machine learning algorithm achieved 90% accuracy in classifying normal versus lymphedematous tissue sites.
Conclusions:
- Optical coherence tomography (OCT) is a promising non-invasive tool for characterizing lymphedema-related skin alterations.
- A machine learning approach utilizing OCT data enables accurate lymphedema detection.
- This study provides a foundation for in vivo monitoring and management of lymphedema.
