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数字量化桥接纤维化和间隔可揭示常规组织学未见的自然史和治疗变化。

Digital quantitation of bridging fibrosis and septa reveals changes in natural history and treatment not seen with conventional histology.

机构信息

Division of Medicine, University College London, London, UK.

Laboratory of Pathology, Post-Mortem Section, National Cancer Institute, Bethesda, Maryland, USA.

出版信息

Liver Int. 2024 Dec;44(12):3214-3228. doi: 10.1111/liv.16092. Epub 2024 Sep 9.

Abstract

BACKGROUND AND AIMS

Metabolic dysfunction-associated steatohepatitis (MASH) with bridging fibrosis is a critical stage in the evolution of fatty liver disease. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence (AI) provides sensitive and reproducible quantitation of liver fibrosis. This methodology was applied to gain an in-depth understanding of intra-stage fibrosis changes and septa analyses in a homogenous, well-characterised group with MASH F3 fibrosis.

METHODS

Paired liver biopsies (baseline [BL] and end of treatment [EOT]) of 57 patients (placebo, n = 17 and tropifexor n = 40), with F3 fibrosis stage at BL according to the clinical research network (CRN) scoring, were included. Unstained sections were examined using SHG/TPEF microscopy with AI. Changes in liver fibrosis overall and in five areas of liver lobules were quantitatively assessed by qFibrosis. Progressive, regressive septa, and 12 septa parameters were quantitatively analysed.

RESULTS

qFibrosis demonstrated fibrosis progression or regression in 14/17 (82%) patients receiving placebo, while the CRN scoring categorised 11/17 (65%) as 'no change'. Radar maps with qFibrosis readouts visualised quantitative fibrosis dynamics in different areas of liver lobules even in cases categorised as 'No Change'. Measurement of septa parameters objectively differentiated regressive and progressive septa (p < .001). Quantitative changes in individual septa parameters (BL to EOT) were observed both in the 'no change' and the 'regression' subgroups, as defined by the CRN scoring.

CONCLUSION

SHG/TPEF microscopy with AI provides greater granularity and precision in assessing fibrosis dynamics in patients with bridging fibrosis, thus advancing knowledge development of fibrosis evolution in natural history and in clinical trials.

摘要

背景与目的

代谢相关脂肪性肝炎伴桥接纤维化(MASH)是脂肪性肝病进展的关键阶段。基于人工智能(AI)的二次谐波产生/双光子激发荧光(SHG/TPEF)显微镜可对肝纤维化进行敏感且可重复的定量检测。该方法应用于对具有 MASH F3 纤维化的同质、特征明确的患者人群,深入了解同一阶段纤维化变化和间隔分析。

方法

共纳入 57 例患者(安慰剂组 n=17,替匹芬净组 n=40)的配对肝活检(基线[BL]和治疗结束[EOT]),根据临床研究网络(CRN)评分,BL 期为 F3 纤维化阶段。采用 SHG/TPEF 显微镜和 AI 对未经染色的切片进行检查。使用 qFibrosis 定量评估肝纤维化总体变化和肝小叶 5 个区域的变化。定量分析进展性、退行性间隔和 12 个间隔参数。

结果

在接受安慰剂治疗的 17 例患者中,14 例(82%)患者的 qFibrosis 显示纤维化进展或逆转,而根据 CRN 评分,11 例(65%)患者归类为“无变化”。qFibrosis 读数的雷达图可视化了肝小叶不同区域的定量纤维化动态,即使在归类为“无变化”的病例中也是如此。客观测量间隔参数可区分退行性和进展性间隔(p<0.001)。在 CRN 评分定义的“无变化”和“逆转”亚组中均观察到个别间隔参数(BL 至 EOT)的定量变化。

结论

基于 AI 的 SHG/TPEF 显微镜在评估桥接纤维化患者的纤维化动态方面提供了更高的粒度和精度,从而推进了对自然史和临床试验中纤维化演变的认识发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c3/11586893/6c781e06ed4d/LIV-44-3214-g005.jpg

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