Stratification of prostate cancer patients into low‐ and high‐grade groups using multiparametric magnetic resonance radiomics with dynamic contrast‐enhanced image joint histograms

Abstract

Purpose: This study aimed to investigate the potential of stratification of prostate cancer patients into low-and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images. Methods: A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image (16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images.Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machine models with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis. Results:The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively. Conclusion:This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low-and high-GGs.

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Parameters Could Predict International Society of Urological Pathology Risk Groups of Prostate Cancers on Radical Prostatectomy

“…In brief, among DCE parameters, the histogram parameters of K trans and k ep can consistently discriminate the aggressiveness of PCa regardless of whether the old GS or new ISUP grading system is used. Recently, Urakami et al [ 14 ] suggested that the integration of mpMRI radiomics with 2D histogram analysis derived from DCE-MRI might stratify patients with PCa into low- and high-risk groups based on their ISUP grades. All of these studies supported the use of DCE parameters as potential predictors for PCa risk stratification.…”

“…Although DCE-MRI may be capable of evaluating the risk of PCa based on GS, the exact correlation between DCE-MRI and the novel ISUP grading system is uncertain because studies investigating this correlation are lacking. Recently, a study on 44 PCa patients with a total of 101 PCa regions proposed that multiparametric MRI (mpMRI) radiomics in conjunction with a two-dimensional (2D) joint histogram generated from DCE images could potentially stratify PCa patients into low- (ISUP grade ≤ II) and high-ISUP grades (ISUP grade ≥ III) [ 14 ], implying that the use of DCE parameters alone may be sufficient for categorizing ISUP grades needing investigation.…”

Analysis of Apparent Diffusion Coefficient Value and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters of Prostate Cancer Patients after Diagnosis and Treatment with Magnetic Resonance Imaging

“…It may be that the outer space between the cells was compressed, which limited the diffusion of water molecules, the MRI signal became higher, and the ADC value decreased. With the increase of PCa cell invasion, the adenoid structure gradually became solid and flaky [ 14 , 15 ]. Ogura et al [ 16 ] pointed out that the higher the degree of PCa malignancy, the lower the ADC value, which was consistent with the results of this study.…”