TY  - JOUR
AU  - Zhdanovich, Yauheniya
AU  - Ackermann, Jörg
AU  - Wild, Peter J
AU  - Köllermann, Jens
AU  - Bankov, Katrin
AU  - Döring, Claudia
AU  - Flinner, Nadine
AU  - Reis, Henning
AU  - Wenzel, Mike
AU  - Höh, Benedikt
AU  - Mandel, Philipp
AU  - Vogl, Thomas J
AU  - Harter, Patrick
AU  - Weber, Katharina
AU  - Koch, Ina
AU  - Bernatz, Simon
TI  - Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology.
JO  - BMC bioinformatics
VL  - 24
IS  - 1
SN  - 1471-2105
CY  - Heidelberg
PB  - Springer
M1  - DKFZ-2023-00019
SP  - 1
PY  - 2023
AB  - 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.Retrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H</td><td width="150">
AB  - E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70
KW  - Machine learning (Other)
KW  - Prediction (Other)
KW  - Prostate cancer (Other)
KW  - Quantitative features (Other)
KW  - Statistical analysis (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:36597019
DO  - DOI:10.1186/s12859-022-05124-9
UR  - https://inrepo02.dkfz.de/record/186570
ER  -