Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease.

IF 5.6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Hepatology Communications Pub Date : 2024-12-11 eCollection Date: 2025-01-01 DOI:10.1097/HC9.0000000000000594
Leticia Khendek, Cyd Castro-Rojas, Constance Nelson, Mosab Alquraish, Rebekah Karns, Jennifer Kasten, Xiao Teng, Alexander G Miethke, Amy E Taylor
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Abstract

Background: Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize that tissue-based analysis of liver fibrosis by second harmonic generation (SHG) microscopy with artificial intelligence analysis can yield prognostic biomarkers in AILD.

Methods: Patients from single-center studies with unstained slides from clinically obtained liver biopsies at AILD diagnosis were identified. Baseline demographics and liver biochemistries at diagnosis and 1 year were collected. Clinical endpoints studied included the presence of varices, variceal bleeding, ascites, HE, and liver transplant. In collaboration with HistoIndex, unstained slides underwent SHG/artificial intelligence analysis to map fibrosis according to 10 quantitative fibrosis parameters based on tissue location, including total, periportal, perisinusoidal, and pericentral area and length of strings.

Results: Sixty-three patients with AIH (51%), primary sclerosing cholangitis (30%), or autoimmune sclerosing cholangitis (19%) at a median of 14 years old (range: 3-24) were included. An unsupervised analysis of quantitative fibrosis parameters representing total and portal fibrosis identified a patient cluster with more primary sclerosing cholangitis/autoimmune sclerosing cholangitis. This group had more fibrosis at diagnosis by METAVIR classification of histopathological review of biopsies (2.5 vs. 2; p = 0.006). This quantitative fibrosis pattern also predicted abnormal 12-month ALT with an OR of 3.6 (1.3-10, p = 0.014), liver complications with an HR of 3.2 (1.3-7.9, p = 0.01), and liver transplantation with an HR of 20.1 (3-135.7, p = 0.002).

Conclusions: The application of SHG/artificial intelligence algorithms in pediatric-onset AILD provides improved insight into liver histopathology through fibrosis mapping. SHG allows objective identification of patients with biliary tract involvement, which may be associated with a higher risk for refractory disease.

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定量纤维化可确定胆道受累情况,并与小儿自身免疫性肝病的预后相关。
背景:患有自身免疫性肝病(AILD)的儿童可能会出现肝纤维化相关并发症,从而需要进行肝移植。我们假设,通过二次谐波发生(SHG)显微镜和人工智能分析对肝纤维化进行基于组织的分析,可以获得 AILD 的预后生物标志物:从单个中心的研究中确定了在确诊AILD时临床获得的肝脏活检未染色切片的患者。收集了基线人口统计学数据以及诊断时和一年后的肝脏生化指标。研究的临床终点包括是否存在静脉曲张、静脉曲张出血、腹水、高血压和肝移植。与HistoIndex公司合作,对未染色的切片进行SHG/人工智能分析,根据组织位置(包括总面积、门脉周围面积、窦周面积、中央周围面积和弦长等10个定量纤维化参数)绘制纤维化图:共纳入63名AIH(51%)、原发性硬化性胆管炎(30%)或自身免疫性硬化性胆管炎(19%)患者,中位年龄为14岁(范围:3-24岁)。通过对代表总纤维化和门脉纤维化的定量纤维化参数进行无监督分析,发现了一个原发性硬化性胆管炎/自身免疫性硬化性胆管炎患者较多的患者群。根据活检组织病理学检查的 METAVIR 分类,这组患者在确诊时纤维化程度更高(2.5 对 2;P = 0.006)。这种定量纤维化模式还可预测12个月ALT异常,OR值为3.6(1.3-10,p = 0.014),肝脏并发症HR值为3.2(1.3-7.9,p = 0.01),肝移植HR值为20.1(3-135.7,p = 0.002):结论:SHG/人工智能算法在小儿AILD中的应用可通过纤维化图谱更好地了解肝脏组织病理学。SHG可客观识别胆道受累的患者,胆道受累可能与较高的难治性疾病风险有关。
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来源期刊
Hepatology Communications
Hepatology Communications GASTROENTEROLOGY & HEPATOLOGY-
CiteScore
8.00
自引率
2.00%
发文量
248
审稿时长
8 weeks
期刊介绍: Hepatology Communications is a peer-reviewed, online-only, open access journal for fast dissemination of high quality basic, translational, and clinical research in hepatology. Hepatology Communications maintains high standard and rigorous peer review. Because of its open access nature, authors retain the copyright to their works, all articles are immediately available and free to read and share, and it is fully compliant with funder and institutional mandates. The journal is committed to fast publication and author satisfaction. ​
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