TY  - JOUR
A1  - Bernatz, Simon
A1  - Ackermann, Jörg
A1  - Mandel, Philipp
A1  - Kaltenbach, Benjamin
A1  - Zhdanovich, Yauheniya
A1  - Harter, Patrick Nikolaus
A1  - Döring, Claudia
A1  - Hammerstingl, Renate Maria
A1  - Bodelle, Boris
A1  - Smith, Kevin
A1  - Bucher, Andreas
A1  - Albrecht, Moritz Hans Ernst
A1  - Rosbach, Nicolas
A1  - Basten, Lajos Maximilian
A1  - Yel, Ibrahim
A1  - Wenzel, Mike
A1  - Bankov, Katrin
A1  - Koch, Ina
A1  - Chun, Felix
A1  - Köllermann, Jens
A1  - Wild, Peter Johannes
A1  - Vogl, Thomas J.
T1  - Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features
T2  - European radiology
N2  - Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
KW  - Prostate cancer
KW  - Multiparametric MRI
KW  - Machine learning
KW  - Artificial intelligence
KW  - Radiomics
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63774
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-637745
SN  - 1432-1084
N1  - Open Access funding provided by Projekt DEAL. This work was supported in part by the LOEWE Center Frankfurt Cancer Institute (FCI) funded by the Hessen State Ministry for Higher Education, Research and the Arts (III L 5 - 519/03/03.001 - (0015)).
N1  - T.J.V., P.J.W., and S.B. would like to thank the Frankfurt Cancer Institute/German Cancer Consortium for the support and funding as part of the Discovery & Development Project 2019.
VL  - 30
IS  - 12
SP  - 6757
EP  - 6769
PB  - Springer
CY  - Berlin ; Heidelberg
ER  -