Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology

Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology

Abstract Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, $$66\pm 6.6$$ 66 ±...

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Main Authors: Yauheniya Zhdanovich, Jörg Ackermann, Peter J. Wild, Jens Köllermann, Katrin Bankov, Claudia Döring, Nadine Flinner, Henning Reis, Mike Wenzel, Benedikt Höh, Philipp Mandel, Thomas J. Vogl, Patrick Harter, Katharina Filipski, Ina Koch, Simon Bernatz
Format: Article
Language:English
Published: BMC 2023-01-01
Series:BMC Bioinformatics
Subjects:
Prostate cancer
Prediction
Quantitative features
Statistical analysis
Machine learning
Online Access:https://doi.org/10.1186/s12859-022-05124-9

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