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DOI 10.1002/prca.201900131

Медицинская библиотека · Proteomics. Clinical applications · 2020

Classification of Inflammatory Bowel Disease from Formalin-Fixed, Paraffin-Embedded Tissue Biopsies via Imaging Mass Spectrometry.

Klein Oliver, Fogt Franz, Hollerbach Stephan, Nebrich Grit, Boskamp Tobias, Wellmann Axel

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

PURPOSE: Discrimination between ulcerative colitis (UC) and Crohn's disease (CD) by histologic features alone can be challenging and often leads to inaccurate initial diagnoses in inflammatory bowel disease (IBD) patients. This is mostly due to an overlap of clinical and histologic features. However, exact diagnosis is not only important for patient treatment but it also has a socioeconomic impact. It is therefore important to develop and improve diagnostic tools complementing traditional histomorphological approaches.

EXPERIMENTAL

DESIGN

In this retrospective proof-of-concept study, the utilization of MALDI imaging is explored in combination with multi-variate data analysis methods to classify formalin-fixed, paraffin-embedded (FFPE) colon biopsies from UC (87 biopsies, 14 patients), CD (71 biopsies, 14 patients), and normal colonic (21 biopsies, 14 patients) tissues.

RESULTS

The proposed method results in an overall balanced accuracy of 85.7% on patient and of 80.4% on sample level, thus demonstrating that the assessment of IBD from FFPE tissue specimens via MALDI imaging is feasible.

CONCLUSIONS AND CLINICAL RELEVANCE: The results emphasize the high potential of this method to distinguish IBD subtypes in FFPE tissue sections, which is a prerequisite for further investigations in retrospective multicenter studies, as well as for a future implementation into clinical routine.

Для цитирования: Klein Oliver, Fogt Franz, Hollerbach Stephan, Nebrich Grit, Boskamp Tobias, Wellmann Axel. Classification of Inflammatory Bowel Disease from Formalin-Fixed, Paraffin-Embedded Tissue Biopsies via Imaging Mass Spectrometry.. Proteomics. Clinical applications. 2020. DOI: 10.1002/prca.201900131.

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