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

  • 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.
Metadaten
Author:Simon Bernatz, Jörg Ackermann, Philipp Mandel, Benjamin KaltenbachGND, Yauheniya Zhdanovich, Patrick Nikolaus HarterORCiDGND, Claudia Döring, Renate Maria Hammerstingl, Boris BodelleORCiDGND, Kevin Smith, Andreas Bucher, Moritz Hans Ernst AlbrechtORCiDGND, Nicolas Rosbach, Lajos Maximilian Basten, Ibrahim YelORCiDGND, Mike WenzelORCiDGND, Katrin Bankov, Ina KochORCiD, Felix ChunORCiDGND, Jens KöllermannORCiDGND, Peter Johannes WildORCiDGND, Thomas J. VoglORCiDGND
URN:urn:nbn:de:hebis:30:3-637745
DOI:https://doi.org/10.1007/s00330-020-07064-5
ISSN:1432-1084
Parent Title (English):European radiology
Publisher:Springer
Place of publication:Berlin ; Heidelberg
Document Type:Article
Language:English
Date of Publication (online):2020/07/16
Date of first Publication:2020/07/16
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/06/03
Tag:Artificial intelligence; Machine learning; Multiparametric MRI; Prostate cancer; Radiomics
Volume:30
Issue:12
Page Number:13
First Page:6757
Last Page:6769
Note:
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)).
Note:
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.
HeBIS-PPN:496362399
Institutes:Informatik und Mathematik
Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0