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Hellen DJ, Fay ME, Lee DH, Klindt-Morgan C, Bennett A, Pachura KJ, Grakoui A, Huppert SS, Dawson PA, Lam WA, Karpen SJ. BiliQML: A supervised machine-learning model to quantify biliary forms from digitized whole-slide liver histopathological images. Am J Physiol Gastrointest Liver Physiol 2024. [PMID: 38651949 DOI: 10.1152/ajpgi.00058.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
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, 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 indicate 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 a novel tool for the characterization of cholangiopathies.
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Affiliation(s)
- Dominick J Hellen
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Meredith E Fay
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Georgia Institute of Technology, Atlanta, GA, United States
| | - David H Lee
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Caroline Klindt-Morgan
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Ashley Bennett
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Kimberly J Pachura
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Arash Grakoui
- Emory National Primate Research Center, Division of Microbiology and Immunology, Emory Vaccine Center, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Stacey S Huppert
- Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Paul A Dawson
- Pediatrics, Emory University, Atlanta, GA, United States
| | - Wilbur A Lam
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Georgia Institute of Technology, Atlanta, GA, United States
| | - Saul J Karpen
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University, Richmond, VA, United States
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