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. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone..

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:30

Enthalten in:

European radiology - 30(2020), 12 vom: 16. Juli, Seite 6757-6769

Sprache:

Englisch

Beteiligte Personen:

Bernatz, Simon [VerfasserIn]
Ackermann, Jörg [VerfasserIn]
Mandel, Philipp [VerfasserIn]
Kaltenbach, Benjamin [VerfasserIn]
Zhdanovich, Yauheniya [VerfasserIn]
Harter, Patrick N. [VerfasserIn]
Döring, Claudia [VerfasserIn]
Hammerstingl, Renate [VerfasserIn]
Bodelle, Boris [VerfasserIn]
Smith, Kevin [VerfasserIn]
Bucher, Andreas [VerfasserIn]
Albrecht, Moritz [VerfasserIn]
Rosbach, Nicolas [VerfasserIn]
Basten, Lajos [VerfasserIn]
Yel, Ibrahim [VerfasserIn]
Wenzel, Mike [VerfasserIn]
Bankov, Katrin [VerfasserIn]
Koch, Ina [VerfasserIn]
Chun, Felix K.-H. [VerfasserIn]
Köllermann, Jens [VerfasserIn]
Wild, Peter J. [VerfasserIn]
Vogl, Thomas J. [VerfasserIn]

Links:

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Themen:

Artificial intelligence
Machine learning
Multiparametric MRI
Prostate cancer
Radiomics

RVK:

RVK Klassifikation

Anmerkungen:

© The Author(s) 2020

doi:

10.1007/s00330-020-07064-5

funding:

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PPN (Katalog-ID):

OLC2120699267

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