Collagen fibers quantification for liver fibrosis assessment using linear dichroism photoacoustic microscopy

Liver fibrosis represents a progressive pathological condition that can culminate in severe hepatic dysfunction, potentially advancing to cirrhosis and liver cancer. The extent of liver fibrosis is intrinsically associated with the quantity of collagen fibers. Although liver biopsy and ultrasound im...

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Bibliographic Details
Main Authors: Yang Qiu, Honghui Li, Kun Yu, Jiali Chen, Li Qi, Yinghua Zhao, Liming Nie
Format: Article
Language:English
Published: Elsevier 2025-04-01
Series:Photoacoustics
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Online Access:http://www.sciencedirect.com/science/article/pii/S2213597925000138
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Summary:Liver fibrosis represents a progressive pathological condition that can culminate in severe hepatic dysfunction, potentially advancing to cirrhosis and liver cancer. The extent of liver fibrosis is intrinsically associated with the quantity of collagen fibers. Although liver biopsy and ultrasound imaging are standard diagnostic tools, their application is constrained by risks of significant complications and variability in different investigators, respectively. In this study, we utilized linear dichroism photoacoustic microscopy (LDPAM) to visualize and quantify collagen fibers, which exhibit specific absorption of polarized light, subsequently calculating a collagen fibers degree of dichroism (CDOD) score. We obtained high-resolution images of liver structures, with an emphasis on collagen fibers within the hepatic tissue. Using the CDOD score, we categorized liver fibrosis into three distinct stages: normal, early, and advanced. For validation purposes, collagen fibers were visualized with Sirius-red staining and quantitatively assessed through the collagen proportional area (CPA) score. Our results demonstrated a significant correlation between the CDOD and CPA scores, with a Pearson coefficient of 0.95. This approach presents a promising and non-invasive method for assessing liver fibrosis by quantifying collagen fibers.
ISSN:2213-5979