PDF(Pubmed)
Abstract：
The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-counting approaches, which are rapidly performed, error prone, and lack architectural context or three-dimensional analysis of the biliary tree in opacified livers, which introduce technical issues along with minimal quantitation. The present study aims to fill these quantitative gaps with a supervised machine-learning model (BiliQML) able to quantify biliary forms in the liver of anti-keratin 19 antibody-stained whole slide images. Training utilized 5,019 researcher-labeled biliary forms, which following feature selection, and algorithm optimization, generated an F score of 0.87. Application of BiliQML on seven separate cholangiopathy models [genetic (Afp-CRE;Pkd1l1null/Fl, Alb-CRE;Rbp-jkfl/fl, and Albumin-CRE;ROSANICD), surgical (bile duct ligation), toxicological (3,5-diethoxycarbonyl-1,4-dihydrocollidine), and therapeutic (Cyp2c70-/- with ileal bile acid transporter inhibition)] allowed for a means to validate the capabilities and utility of this platform. The results from BiliQML quantification revealed biological and pathological differences across these seven diverse models, indicating a highly sensitive, robust, and scalable methodology for the quantification of distinct biliary forms. BiliQML is the first comprehensive machine-learning platform for biliary form analysis, adding much-needed morphologic context to standard immunofluorescence-based histology, and provides clinical and basic science researchers with a novel tool for the characterization of cholangiopathies.NEW & NOTEWORTHY BiliQML is the first comprehensive machine-learning platform for biliary form analysis in whole slide histopathological images. This platform provides clinical and basic science researchers with a novel tool for the improved quantification and characterization of biliary tract disorders.
摘要：
有限的可用定量方法阻碍了在发育过程中和损伤后针对胆管细胞和胆管树的研究进展。目前的技术包括二维标准组织学细胞计数方法，快速执行容易出错且缺乏建筑环境；或不透明肝脏中胆道树的三维分析，它引入了技术问题和最小的量化。本研究旨在通过监督机器学习模型（BiliQML）来填补这些定量空白，该模型能够量化抗角蛋白19抗体染色的整个载玻片图像的肝脏中的胆道形式。培训使用了5,019种研究人员标记的胆道形式，在特征选择之后，和算法优化，产生的F分数为0.87。BiliQML在七个单独的胆管病模型上的应用；遗传（Afp-CRE;Pkd1l1null/Fl，Alb-CRE;Rbp-jkfl/fl,白蛋白-CRE；ROSANICD)，手术（胆管结扎），毒理学（3,5-二乙氧基羰基-1,4-二氢可力丁），和治疗性（Cyp2c70-/-回肠胆汁酸转运蛋白抑制），允许一种方法来验证功能，和这个平台的实用性。BiliQML定量结果显示，这七个不同模型的生物学和病理学差异表明，健壮,和可扩展的方法来量化不同的胆道形式。BiliQML是第一个用于胆道形态分析的综合机器学习平台，为标准的基于免疫荧光的组织学添加了急需的形态学背景，并为临床和基础科学研究人员提供了一种表征胆管疾病的新工具。
