Multiparametric MRI-based nomograms in predicting positive surgical margins of prostate cancer after laparoscopic radical prostatectomy

Abstract

BackgroundPositive surgical margins (PSMs) are an independent risk factor of biochemical recurrence in patients with prostate cancer (PCa) after laparoscopic radical prostatectomy; however, limited MRI-based predictive tools are available. This study aimed to develop a novel nomogram combining clinical and multiparametric MRI (mpMRI) parameters to reduce PSMs by improving surgical planning.MethodsOne hundred and three patients with PCa (55 patients with negative surgical margins [NSMs] and 48 patients with PSMs) were included in this retrospective study. The following parameters were obtained using GE Functool post-processing software: diffusion-weighted imaging (DWI); intravoxel incoherent motion model (IVIM); and diffusion kurtosis imaging (DKI). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to analyze the data set to select the optimal MRI predictors. Preoperatively clinical parameters used to build a clinical nomogram (C-nomogram). Multivariable logistic regression analysis was used to build an MRI nomogram (M-nomogram) by introducing the MRI parameters. Based on the MRI and clinical parameters, build an MRI combined with clinical parameters nomogram (MC-nomogram). Comparisons with the M-nomogram and MC-nomogram were based on discrimination, calibration, and decision curve analysis (DCA). A 3-fold cross-validation method was used to assess the stability of the nomogram.ResultsThere was no statistical difference in AUC between the C-nomogram (sensitivity=64%, specificity=65% and AUC=0.683), the M-nomogram (sensitivity=57%, specificity=88% and AUC=0.735) and the MC-nomogram (sensitivity= 64%, specificity=82% and AUC=0.756). The calibration curves of the three nomograms used to predict the risk of PSMs in patients with PCa showed good agreement. The net benefit of the MC-nomogram was higher than the others (range, 0.2-0.7).ConclusionsThe mpMRI-based nomogram can predict PSMs in PCa patients. Although its AUC (0.735) is not statistically different from that of the clinical-based nomogram AUC (0.683). However, mpMRI-based nomogram has higher specificity (88% VS. 63%), model stability, and clinical benefit than clinical-based nomogram. And the predictive ability of mpMRI plus clinical parameters for PSMs is further improved.

Value of three-dimensional visualization of preoperative prostatic magnetic resonance imaging based on measurements of anatomical structures in predicting positive surgical margin after radical prostatectomy

“…The above results are consistent with the findings of Song C et al, who also mentioned limitations in tumor volume measurements and utilized calculated volumes rather than actual volumes. Our study has overcome the above limitation with 3D technology, making the results more convincing (55). The reason that no significance was found in the multivariate analysis might be due to the larger tumor having closer proximity to the prostate apex, which is another important finding of ours: patients with a longer UD had a lower incidence of PSM and APSM.…”

“…Only a few previous studies have performed nomograms for the preoperative prediction of PSM, and only correlations regarding clinical information and conventional MRI have been explored ( 14 , 56 ). In this study, 3D visualization technology was combined with significant clinical data and imaging parameters based on MRI to improve the predictive capability and accuracy of the nomograms.…”

An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy

“…Some previous studies have also tried to propose MRI-based grading systems for PSM. Chen et al [ 10 ] developed an MRI-based tool for PSM, with AUC, sensitivity, and specificity of 0.735, 57%, and 88%, respectively. However, their model was based on complex quantitative imaging features from DWI, intravoxel incoherent motion model, and diffusion kurtosis imaging, which hampered its application in a wider patient population.…”

“…Many tumor-related imaging features, such as tumor location determined by MRI, Prostate Imaging-Reporting and Data System (PI-RADS) category, length of capsular tumor contact, and distance of the lesion to the membranous urethra, have been reported to correlate with PSM [ 18 – 20 ]. Despite these independent imaging features, some studies have also proposed MRI-based grading systems or nomograms for preoperative prediction of PSM [ 10 , 21 ]. However, these methods are complex to some extent to be used in clinical practice.…”