Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Abstract

Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

“…Chaddad et al [ 23 ] found a significant correlation between GLCM features extracted jointly from T2w and ADC images and Gleason score, with an AUC of 0.78 for GS ≤ 3, 0.82 for GS 3+4 and 0.65 for GS ≥ 4+3. Monti et al [ 24 ] found that for PCa detection, standard radiomics models using T2w and ADC images performed better than an advanced model with additional diffusion kurtosis imaging and DCE. Approaches extracting radiomic features from prostate mpMRI using auto-fixed VOIs have also achieved promising results on peripheral zone csPCa detection, with the highest AUC of 0.87 for the XGBoost classifier [ 19 ].…”

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

“…These results suggest that the inclusion of radiomic features derived from DCE-MRI does not provide a clear added value for PCa detection of PI-RADS 3 lesions. On one hand, this would justify the choice to exclude DCE-MRI from dominant sequences for PI-RADSv2 and PI-RADS v2.1 score assessment 17 , 24 , 45 . Similar considerations also apply to upPI-RADS 4 classification task, for which DCE-MRI features did not even survive in the univariate analysis preceding the mRMR step.…”

“…Further analysis on original DCE-MRI images 57 and/or maps of pharmacokinetic parameters 58 may be investigated, helping to overcome controversies related to DCE-MRI and clarify its role in PCa management. It could be also interesting to investigate if performances of prediction models for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions could improve adding features arising from advanced diffusion models prediction model, which were found to be promising for detection and characterization of PCa, even if their role is not clearly affirmed due to the lack of a standardized diffusion MRI protocol 17 , 59 . Another aspect to highlight is that also delineations of prostatic lesions are prone to inter-observer variability.…”

What Is the Role of Imaging in Cancers?

“…Table 1 shows a summary of the topics and the papers included for each topic [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The majority of papers are focused on prostate cancer (PCa) and radiomics.…”

“…Furthermore, in this latter setting of patients, a segmentation-based tumor load at 99mTc-dysphonate SPECT/CT was linked with clinical outcome [ 21 ]. Finally, a radiomic approach with a specific magnetic resonance imaging (MRI) protocol can be useful to appropriately detect and characterize PCa [ 31 ]. Radiomics is an emerging field, defined as the extraction of quantitative data from medical images by using specific software.…”