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 (DWIb2000) 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. DWIb2000 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 DWIb2000 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 DWIb2000 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-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.
Highlights
Prostate cancer (PCa) is the most common malignancy diagnosed in men worldwide [1]
Even if there is no consensus regarding the optimum management of localized disease, ncsPCa was adopted as components of the “very low-risk category” of the National Comprehensive Cancer Network guidelines in which active surveillance (AS) protocol is supported as a management option
We investigate the effectiveness of DWIb2000 sequences in quantitative tissue characterization through a predictive radiomic model developed to detect clinically significant prostate cancer (csPCa) in patients with Gleason score (GS) > 3 + 3, exploiting only image-based features, compared with apparent diffusion coefficient (ADC) performing the same task
Summary
Prostate cancer (PCa) is the most common malignancy diagnosed in men worldwide [1]. This strongly impacts clinical management in terms of costs and resources, based on the PCa stage at the diagnosis that could suggest different clinical pathways [2]. AS is a strategy of close monitoring, typically using PSA, repeat biopsies and multiparametric magnetic resonance imaging (mpMRI), keeping curative treatment for those with evidence of disease. It has been recommended for men with low-risk disease. CsPCa may be subjected to curative options that include prostatectomy (RP), external beam radiotherapy (RT) or low-dose-rate brachytherapy [5]
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