Universität zu Köln

Eminaga, Okyaz, Al-Hamad, Omran, Boegemann, Martin, Breil, Bernhard and Semjonow, Axel (2020). Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer. Health Inform. J., 26 (2). S. 945 - 963. THOUSAND OAKS: SAGE PUBLICATIONS INC. ISSN 1741-2811

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Abstract

This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient's subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Eminaga, Okyaz UNSPECIFIED UNSPECIFIED UNSPECIFIED Al-Hamad, Omran UNSPECIFIED UNSPECIFIED UNSPECIFIED Boegemann, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Breil, Bernhard UNSPECIFIED UNSPECIFIED UNSPECIFIED Semjonow, Axel UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-331245
DOI:	10.1177/1460458219855884
Journal or Publication Title:	Health Inform. J.
Volume:	26
Number:	2
Page Range:	S. 945 - 963
Date:	2020
Publisher:	SAGE PUBLICATIONS INC
Place of Publication:	THOUSAND OAKS
ISSN:	1741-2811
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language BIOPSY GLEASON SCORE; PREDICTION MODEL; RADICAL PROSTATECTOMY; ACTIVE SURVEILLANCE; PATHOLOGICAL STAGE; PARTIN TABLES; VALIDATION; RISK; SURVIVAL; MEN Multiple languages Health Care Sciences & Services; Medical Informatics Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/33124