TY  - JOUR
A1  - Zhdanovich, Yauheniya
A1  - Ackermann, Jörg
A1  - Wild, Peter Johannes
A1  - Köllermann, Jens
A1  - Bankov, Katrin
A1  - Döring, Claudia
A1  - Flinner, Nadine
A1  - Reis, Henning
A1  - Wenzel, Mike
A1  - Höh, Robert Benedikt
A1  - Mandel, Philipp
A1  - Vogl, Thomas J.
A1  - Harter, Patrick Nikolaus
A1  - Filipski, Katharina Johanna
A1  - Koch, Ina
A1  - Bernatz, Simon
T1  - Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology
T2  - BMC bioinformatics
N2  - Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered 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&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% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
KW  - Prostate cancer
KW  - Prediction
KW  - Quantitative features
KW  - Statistical analysis
KW  - Machine learning
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/75207
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-752073
SN  - 1471-2105
N1  - The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
N1  - Open Access funding enabled and organized by Projekt DEAL.
N1  - Funding: LOEWE Center Frankfurt Cancer Institute (FCI) ; III L 5 - 519/03/03.001 - (0015)
N1  - Funding: Frankfurt Research Funding (FFF) ; program Nachwuchswissenschaftler
N1  - Gefördert durch den Open-Access-Publikationsfonds der Goethe-Universität
N1  - Funding: Mildred-Scheel Founation ; Clinical Scientist Program
VL  - 24.2023
IS  - art. 1
SP  - 1
EP  - 14
PB  - BioMed Central ; Springer
CY  - London ; Berlin ; Heidelberg
ER  -