Pancreatic magnetic resonance imaging texture analysis in chronic pancreatitis: a feasibility and validation study.
Purpose: This feasibility and validation study addresses the potential use of magnetic resonance imaging (MRI) texture analysis of the pancreas in patients with chronic pancreatitis (CP). Methods: Extraction of 851 MRI texture features from diffusion weighted imaging (DWI) of the pancreas was perfor...
| Publicado en: | Abdominal Radiology Vol. 45; no. 5; pp. 1497 - 1507 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142848414&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142848414 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: May2020 vid: 45 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142848414 10.1007/s00261-020-02512-8 142848414 ppf: 1497 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Pancreatic magnetic resonance imaging texture analysis in chronic pancreatitis: a feasibility and validation study. aug: au: Frøkjær, Jens Brøndum Lisitskaya, Maria Valeryevna Jørgensen, Alex Skovsbo Østergaard, Lasse Riis Hansen, Tine Maria Drewes, Asbjørn Mohr Olesen, Søren Schou affil: Department of Radiology, Aalborg University Hospital, P.O. Box 365, 9100, Aalborg, Denmark sug: ab: Purpose: This feasibility and validation study addresses the potential use of magnetic resonance imaging (MRI) texture analysis of the pancreas in patients with chronic pancreatitis (CP). Methods: Extraction of 851 MRI texture features from diffusion weighted imaging (DWI) of the pancreas was performed in 77 CP patients and 22 healthy controls. Features were reduced to classify patients into subgroups, and a Bayes classifier was trained using a tenfold cross-validation forward selection procedure. The classifier was optimized to obtain the best average m-fold accuracy, sensitivity, specificity, and positive predictive value. Classifiers were: presence of disease (CP vs. healthy controls), etiological risk factors (alcoholic vs. nonalcoholic etiology of CP and tobacco use vs. no tobacco use), and complications to CP (presumed pancreatogenic diabetes vs. no diabetes and pancreatic exocrine insufficiency vs. normal pancreatic function). Results: The best classification performance was obtained for the disease classifier selecting only five of the original features with 98% accuracy, 97% sensitivity, 100% specificity, and 100% positive predictive value. The risk factor classifiers obtained good performance using 9 (alcohol: 88% accuracy) and 10 features (tobacco: 86% accuracy). The two complication classifiers obtained similar accuracies with only 4 (diabetes: 83% accuracy) and 3 features (exocrine pancreatic function: 82% accuracy). Conclusion: Pancreatic texture analysis demonstrated to be feasible in patients with CP and discriminate clinically relevant subgroups based on etiological risk factors and complications. In future studies, the method may provide useful information on disease progression (monitoring) and detection of biomarkers characterizing early-stage CP. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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