Evaluation of grade and invasiveness of bladder urothelial carcinoma using infrared imaging and machine learning-Reference-Cited by-同舟云学术

Evaluation of grade and invasiveness of bladder urothelial carcinoma using infrared imaging and machine learning

Published:2023 Issue:2 Volume:148 Page:278-285
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Kujdowicz Monika¹²^ORCID,Perez-Guaita David³^ORCID,Chlosta Piotr⁴^ORCID,Okon Krzysztof¹^ORCID,Malek Kamilla²^ORCID

Affiliation:

1. Department of Pathomorphology, Faculty of Medicine, Jagiellonian University Medical College, Krakow, Grzegorzecka 16, 31-531, Poland

2. Faculty of Chemistry, Jagiellonian University in Krakow, Krakow, Gronostajowa 2, 30-387, Poland

3. Department of Analytical Chemistry, University of Valencia, 50 Dr. Moliner Street, Research Building, 46100 Burjassot, Valencia, Spain

4. Department of Urology, Medical Faculty, Jagiellonian University Medical College, Krakow, Jakubowskiego 2, 30-688, Poland

Abstract

We combined infrared (IR) microscopy of bladder sections with machine learning (partial least squares-discriminant analysis) to provide an automated methodology for the prediction of the presence of cancer, invasiveness and grade.

Funder

Ministerio de Ciencia e Innovación

Narodowe Centrum Badań i Rozwoju

Narodowe Centrum Nauki

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/AN/D2AN01583H

Reference40 articles.

1. Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends

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3. An interobserver reproducibility study on invasiveness of bladder cancer using virtual microscopy and heatmaps

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