An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

Autor: Sallis BF; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass., Erkert L; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass., Moñino-Romero S; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria., Acar U; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass., Wu R; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass., Konnikova L; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass., Lexmond WS; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass., Hamilton MJ; Department of Medicine, Harvard Medical School, Boston, Mass; Department of Pathology, Brigham and Women's Hospital, Boston, Mass; Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Mass., Dunn WA; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass., Szepfalusi Z; Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria., Vanderhoof JA; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass., Snapper SB; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass., Turner JR; Department of Pathology, Brigham and Women's Hospital, Boston, Mass; Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Mass., Goldsmith JD; Department of Pathology, Boston Children's Hospital, Boston, Mass., Spencer LA; Department of Medicine, Harvard Medical School, Boston, Mass; Department of Medicine, Division of Allergy and Inflammation, Beth Israel Deaconess Medical Center, Boston, Mass., Nurko S; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass., Fiebiger E; Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass. Electronic address: edda.fiebiger@childrens.harvard.edu.
Jazyk: angličtina
Zdroj: The Journal of allergy and clinical immunology [J Allergy Clin Immunol] 2018 Apr; Vol. 141 (4), pp. 1354-1364.e9. Date of Electronic Publication: 2017 Dec 19.
DOI: 10.1016/j.jaci.2017.11.027
Abstrakt: Background: Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status.
Objective: This study sought to establish an automated medical algorithm to assist in the evaluation of EoE.
Methods: Machine learning techniques were used to establish a diagnostic probability score for EoE, p(EoE), based on esophageal mRNA transcript patterns from biopsies of patients with EoE, gastroesophageal reflux disease and controls. Dimensionality reduction in the training set established weighted factors, which were confirmed by immunohistochemistry. Following weighted factor analysis, p(EoE) was determined by random forest classification. Accuracy was tested in an external test set, and predictive power was assessed with equivocal patients. Esophageal IgE production was quantified with epsilon germ line (IGHE) transcripts and correlated with serum IgE and the T h 2-type mRNA profile to establish an IGHE score for tissue allergy.
Results: In the primary analysis, a 3-class statistical model generated a p(EoE) score based on common characteristics of the inflammatory EoE profile. A p(EoE) ≥ 25 successfully identified EoE with high accuracy (sensitivity: 90.9%, specificity: 93.2%, area under the curve: 0.985) and improved diagnosis of equivocal cases by 84.6%. The p(EoE) changed in response to therapy. A secondary analysis loop in EoE patients defined an IGHE score of ≥37.5 for a patient subpopulation with increased esophageal allergic inflammation.
Conclusions: The development of intelligent data analysis from a machine learning perspective provides exciting opportunities to improve diagnostic precision and improve patient care in EoE. The p(EoE) and the IGHE score are steps toward the development of decision trees to define EoE subpopulations and, consequently, will facilitate individualized therapy.
(Copyright © 2017 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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