A Review of Engineering Approaches for Lymphedema Detection

An Evaluation of Lymphedema Using Optical Coherence Tomography: A Rat Limb Model Approach

Lymphedema is a pathology caused by poor lymphatic flow which may lead to complete disability. Currently, precise, non-invasive techniques for quantifying lymphedema are lacking. In this paper, the results of an in vivo assessment of lymphedema via a developed small-animal model using the hindlimbs of rats and an optical coherence tomography (OCT) technique are presented. This model of lymphedema was based on a surgical lymph node resection and subsequent two-step X-ray exposure. The development of lymphedema was verified via the histological examination of tissue biopsies. The properties of the lymphedematous skin were analyzed in vivo and compared with healthy skin via OCT. The main differences observed were (1) a thickening of the stratum corneum layer, (2) a thinning of the viable epidermis layer, and (3) higher signal attenuation in the dermis layer of the lymphedematous skin. Based on the distribution of the OCT signal's intensity in the skin, a machine learning algorithm was developed which allowed for a classification of normal and lymphedematous tissue sites with an accuracy of 90%. The obtained results pave the way for in vivo control over the development of lymphedema.

The Unresolved Pathophysiology of Lymphedema

Lymphedema is the clinical manifestation of impaired lymphatic transport. It remains an under-recognized and under-documented clinical condition that still lacks a cure. Despite the substantial advances in the understanding of lymphatic vessel biology and function in the past two decades, there are still unsolved questions regarding the pathophysiology of lymphedema, especially in humans. As a consequence of impaired lymphatic drainage, proteins and lipids accumulate in the interstitial space, causing the regional tissue to undergo extensive and progressive architectural changes, including adipose tissue deposition and fibrosis. These changes are also associated with inflammation. However, the temporal sequence of these events, the relationship between these events, and their interplay during the progression are not clearly understood. Here, we review our current knowledge on the pathophysiology of lymphedema derived from human and animal studies. We also discuss the possible cellular and molecular mechanisms involved in adipose tissue and collagen accumulation during lymphedema. We suggest that more studies should be dedicated to enhancing our understanding of the human pathophysiology of lymphedema to pave the way for new diagnostic and therapeutic avenues for this condition.

Application of multiphoton imaging and machine learning to lymphedema tissue analysis

The results of in-vivo two-photon imaging of lymphedema tissue are presented. The study involved 36 image samples from II stage lymphedema patients and 42 image samples from healthy volunteers. The papillary layer of the skin with a penetration depth of about 100 μm was examined. Both the collagen network disorganization and increase of the collagen/elastin ratio in lymphedema tissue, characterizing the severity of fibrosis, was observed. Various methods of image characterization, including edge detectors, a histogram of oriented gradients method, and a predictive model for diagnosis using machine learning, were used. The classification by "ensemble learning" provided 96% accuracy in validating the data from the testing set.