High-throughput, machine learning-based quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with nonalcoholic fatty liver disease
| Autor: | Alexandros T. Tzallas, Markos G. Tsipouras, Roberta Forlano, Nikolaos Giannakeas, James Maurice, Mark Thursz, Robert D. Goldin, N. Angkathunyakul, Benjamin H. Mullish, Pinelopi Manousou, J. Lloyd, Michael Yee |
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| Přispěvatelé: | Medical Research Council, Medical Research Council (MRC), European Association for the Study of Liver, Imperial College Healthcare NHS Trust- BRC Funding |
| Rok vydání: | 2019 |
| Předmět: |
Liver Cirrhosis
Ballooning% ballooning percentage Intraclass correlation Biopsy JTT Jonckheere–Terpstra test Inflammation% inflammation percentage computer.software_genre Severity of Illness Index Machine Learning 0302 clinical medicine Non-alcoholic Fatty Liver Disease Interquartile range Fibrosis Nonalcoholic fatty liver disease Medicine Diagnostics medicine.diagnostic_test Gastroenterology NASH Fat% fat percentage Liver 030220 oncology & carcinogenesis Liver biopsy 030211 gastroenterology & hepatology CPA collagen proportionate area NASH nonalcoholic steatohepatitis Machine learning Article ICC interclass correlation coefficient 03 medical and health sciences Artificial Intelligence Humans NASH CRN NASH CRN Nonalcoholic Steatohepatitis Clinical Research Network Grading (tumors) IQR interquartile range Inflammation Hepatology Gastroenterology & Hepatology business.industry 1103 Clinical Sciences medicine.disease FU follow-up evaluation NAFLD nonalcoholic fatty liver disease Artificial intelligence Steatosis business computer NAS nonalcoholic fatty liver disease activity score |
| Zdroj: | 2090.e9 Clinical Gastroenterology and Hepatology |
| Popis: | Background & Aims Liver biopsy is the reference standard for staging and grading nonalcoholic fatty liver disease (NAFLD), but histologic scoring systems are semiquantitative with marked interobserver and intraobserver variation. We used machine learning to develop fully automated software for quantification of steatosis, inflammation, ballooning, and fibrosis in biopsy specimens from patients with NAFLD and validated the technology in a separate group of patients. Methods We collected data from 246 consecutive patients with biopsy-proven NAFLD and followed up in London from January 2010 through December 2016. Biopsy specimens from the first 100 patients were used to derive the algorithm and biopsy specimens from the following 146 were used to validate it. Biopsy specimens were scored independently by pathologists using the Nonalcoholic Steatohepatitis Clinical Research Network criteria and digitalized. Areas of steatosis, inflammation, ballooning, and fibrosis were annotated on biopsy specimens by 2 hepatobiliary histopathologists to facilitate machine learning. Images of biopsies from the derivation and validation sets then were analyzed by the algorithm to compute percentages of fat, inflammation, ballooning, and fibrosis, as well as the collagen proportionate area, and compared with findings from pathologists’ manual annotations and conventional scoring systems. Results In the derivation group, results from manual annotation and the software had an interclass correlation coefficient (ICC) of 0.97 for steatosis (95% CI, 0.95–0.99; P < .001); ICC of 0.96 for inflammation (95% CI, 0.9–0.98; P < .001); ICC of 0.94 for ballooning (95% CI, 0.87–0.98; P |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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