Suppr超能文献

数字病理学可实现慢性肝病患者炎症活动的自动化和定量评估。

Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease.

机构信息

Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, 46010 Valencia, Spain.

Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, 46026 Valencia, Spain.

出版信息

Biomolecules. 2021 Dec 2;11(12):1808. doi: 10.3390/biom11121808.

Abstract

Traditional histological evaluation for grading liver disease severity is based on subjective and semi-quantitative scores. We examined the relationship between digital pathology analysis and corresponding scoring systems for the assessment of hepatic necroinflammatory activity. A prospective, multicenter study including 156 patients with chronic liver disease (74% nonalcoholic fatty liver disease-NAFLD, 26% chronic hepatitis-CH etiologies) was performed. Inflammation was graded according to the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network system and METAVIR score. Whole-slide digital image analysis based on quantitative (I-score: inflammation ratio) and morphometric (C-score: proportionate area of staining intensities clusters) measurements were independently performed. Our data show that I-scores and C-scores increase with inflammation grades ( < 0.001). High correlation was seen for CH ( = 0.85-0.88), but only moderate for NAFLD ( = 0.5-0.53). I-score ( = 0.008) and C-score ( = 0.002) were higher for CH than NAFLD. Our MATLAB algorithm performed better than QuPath software for the diagnosis of low-moderate inflammation ( < 0.05). C-score AUC for classifying NASH was 0.75 (95%CI, 0.65-0.84) and for moderate/severe CH was 0.99 (95%CI, 0.97-1.00). Digital pathology measurements increased with fibrosis stages ( < 0.001). In conclusion, quantitative and morphometric metrics of inflammatory burden obtained by digital pathology correlate well with pathologists' scores, showing a higher accuracy for the evaluation of CH than NAFLD.

摘要

传统的肝脏疾病严重程度分级的组织学评估基于主观和半定量评分。我们研究了数字病理学分析与评估肝坏死性炎症活动的相应评分系统之间的关系。进行了一项前瞻性、多中心研究,纳入了 156 例慢性肝病患者(74%非酒精性脂肪性肝病-NAFLD,26%慢性肝炎-CH 病因)。炎症根据非酒精性脂肪性肝炎(NASH)临床研究网络系统和 METAVIR 评分进行分级。基于定量(I 评分:炎症比)和形态计量(C 评分:染色强度聚类的比例面积)测量的全幻灯片数字图像分析独立进行。我们的数据表明,I 评分和 C 评分随炎症等级增加而增加(<0.001)。CH 之间具有高度相关性(=0.85-0.88),但仅为中度相关性(=0.5-0.53)。CH 的 I 评分(=0.008)和 C 评分(=0.002)高于 NAFLD。我们的 MATLAB 算法在诊断低-中度炎症方面优于 QuPath 软件(<0.05)。用于诊断 NASH 的 C 评分 AUC 为 0.75(95%CI,0.65-0.84),用于诊断中度/重度 CH 的 AUC 为 0.99(95%CI,0.97-1.00)。数字病理学测量值随纤维化阶段增加(<0.001)。总之,数字病理学获得的炎症负担的定量和形态计量学指标与病理学家评分密切相关,在评估 CH 方面比 NAFLD 具有更高的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc78/8699191/de8f9f6be4d9/biomolecules-11-01808-g0A1a.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验