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Tytuł pozycji:

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.

Tytuł :
Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.
Autorzy :
Woerl AC; Institute of Pathology, University Medical Center Mainz, Mainz, Germany; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
Eckstein M; Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Geiger J; Institute of Pathology, University Medical Center Mainz, Mainz, Germany; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
Wagner DC; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Daher T; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Stenzel P; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Fernandez A; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Hartmann A; Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Wand M; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
Roth W; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Foersch S; Institute of Pathology, University Medical Center Mainz, Mainz, Germany. Electronic address: .
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Źródło :
European urology [Eur Urol] 2020 Aug; Vol. 78 (2), pp. 256-264. Date of Electronic Publication: 2020 Apr 27.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
Język :
English
Imprint Name(s) :
Publication: 2002- : Amsterdam : Elsevier Science
Original Publication: Basel, New York, Karger.
MeSH Terms :
Deep Learning*
Urinary Bladder Neoplasms/*classification
Urinary Bladder Neoplasms/*genetics
Forecasting ; Humans ; Molecular Diagnostic Techniques ; Neoplasm Invasiveness ; Urinary Bladder Neoplasms/diagnosis ; Urinary Bladder Neoplasms/pathology
Contributed Indexing :
Keywords: Deep learning*; Histopathology*; Molecular subtype*; Muscle-invasive bladder cancer*
Entry Date(s) :
Date Created: 20200502 Date Completed: 20210624 Latest Revision: 20220112
Update Code :
20220112
DOI :
10.1016/j.eururo.2020.04.023
PMID :
32354610
Czasopismo naukowe
Background: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications.
Objective: In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning.
Design, Setting, and Participants: Two cohorts of patients with MIBC were used: (1) The Cancer Genome Atlas Urothelial Bladder Carcinoma dataset including 407 patients and (2) our own cohort including 16 patients with treatment-naïve, primary resected MIBC. This resulted in a total of 423 digital whole slide images of tumor tissue to train, validate, and test the deep learning algorithm to predict the molecular subtype.
Outcome Measurements and Statistical Analysis: Various accuracy measurements including the area under the receiver operating characteristic curves were used to evaluate the deep learning model. A sliding window approach to visualize classification was used. Class activation maps were used to identify image features that are most relevant to call a specific class.
Results and Limitations: The deep learning model showed great performance in the prediction of the molecular subtype of MIBC patients from hematoxylin and eosin (HE) slides alone-similar to or better than pathology experts. Using different visualization techniques, we identified new histopathological features that were most relevant to our model.
Conclusions: Deep learning can be used to predict important molecular features in MIBC patients from HE slides alone, potentially improving the clinical management of this disease significantly.
Patient Summary: In patients with bladder cancer, a computer program found changes in the appearance of tumor tissue under the microscope and used these to predict genetic alterations. This could potentially benefit patients.
(Copyright © 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.)
Comment in: Nat Rev Urol. 2020 Aug;17(8):432. (PMID: 32467577)

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