Literature DB >> 15194975

Quantifying fibrosis in venous disease: mechanical properties of lipodermatosclerotic and healthy tissue.

Mary Jo Geyer1, David M Brienza, Vikram Chib, Jue Wang.   

Abstract

OBJECTIVES: To quantify the mechanical properties of medioposterior bulk calf tissue in patients with lipodermatosclerotic venous-insufficient tissue and individuals with apparently healthy tissue using a novel ultrasound indentometry method, and to identify parameters with the potential for quantifying fibrosis in subsequent studies.
DESIGN: 2-group, quasi-experimental design
SETTING: Soft Tissue Mechanics Laboratory, University of Pittsburgh, Pittsburgh, PA PARTICIPANTS: 9 healthy and 9 venous-insufficient individuals aged 35 to 85 years
INTERVENTIONS: Ultrasound indentometry and computed tomography (CT) of calf tissue MAIN OUTCOME MEASURES: Between group differences and associations among quasi-linear viscoelastic (QLV) tissue parameters and CT descriptors MAIN
RESULTS: Established the accuracy, validity, and reliability of the QLV model and ultrasound indentometry method. Demonstrated a range of significant differences between the groups (P <.020 to P <.004) for selected QLV parameters. Also found significant correlations between CT measures of fibrosis and dermal thickness and QLV elastic measures (P <.034 to P <.005).
CONCLUSION: Attempts to quantify fibrosis in lipodermatosclerosis have included histologic exams, palpation/pitting, durometer readings, and imaging techniques, but these efforts have failed to produce a clinically practical, noninvasive method. A novel ultrasound indentometry method was used to acquire in vivo data from which tissue parameters were derived. These data support the further development of ultrasound indentometry as a method to quantify fibrosis in venous disease.

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Year:  2004        PMID: 15194975     DOI: 10.1097/00129334-200404000-00014

Source DB:  PubMed          Journal:  Adv Skin Wound Care        ISSN: 1527-7941            Impact factor:   2.347


  3 in total

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Authors:  Liang Zhai; Mark L Palmeri; Richard R Bouchard; Roger W Nightingale; Kathryn R Nightingale
Journal:  Ultrason Imaging       Date:  2008-04       Impact factor: 1.578

2.  Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images.

Authors:  Kyo-In Koo; Chang Ho Hwang; Hyewon Son; Suwon Lee; Kwangsoo Kim
Journal:  Sci Rep       Date:  2022-09-13       Impact factor: 4.996

3.  Comparison of a novel algorithm quantitatively estimating epifascial fibrosis in three-dimensional computed tomography images to other clinical lymphedema grading methods.

Authors:  Kyo-In Koo; Myoung-Hwan Ko; Yongkwan Lee; Hye Won Son; Suwon Lee; Chang Ho Hwang
Journal:  PLoS One       Date:  2019-12-10       Impact factor: 3.240

  3 in total

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