The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Bevilacqua, Alessandro; Mottola, Margherita; Ferroni, Fabio; Rossi, Alice; Gavelli, Giampaolo; Barone, Domenico

doi:10.3390/diagnostics11050739

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The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Alessandro Bevilacqua

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Department of Computer Science and Engineering (DISI), University of Bologna, Viale Risorgimento 2, I-40136 Bologna, Italy

Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Via Toffano 2/2, I-40125 Bologna, Italy

Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Viale Risorgimento 2, I-40136 Bologna, Italy

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IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Via Piero Maroncelli 40, I-47014 Meldola, Italy

Author to whom correspondence should be addressed.

Academic Editor: Damiano Caruso

Diagnostics 2021, 11(5), 739; https://doi.org/10.3390/diagnostics11050739

Received: 5 March 2021 / Revised: 19 April 2021 / Accepted: 20 April 2021 / Published: 21 April 2021

(This article belongs to the Special Issue Radiomics in Oncology)

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Abstract

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWI_b2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWI_b2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWI_b2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWI_b2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWI_b2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection. View Full-Text

Keywords: prostate cancer; radiomics; machine learning; tumor staging; cancer heterogeneity; image processing

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MDPI and ACS Style

Bevilacqua, A.; Mottola, M.; Ferroni, F.; Rossi, A.; Gavelli, G.; Barone, D. The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer. Diagnostics 2021, 11, 739. https://doi.org/10.3390/diagnostics11050739

AMA Style

Bevilacqua A, Mottola M, Ferroni F, Rossi A, Gavelli G, Barone D. The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer. Diagnostics. 2021; 11(5):739. https://doi.org/10.3390/diagnostics11050739

Chicago/Turabian Style

Bevilacqua, Alessandro, Margherita Mottola, Fabio Ferroni, Alice Rossi, Giampaolo Gavelli, and Domenico Barone. 2021. "The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer" Diagnostics 11, no. 5: 739. https://doi.org/10.3390/diagnostics11050739

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The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

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