A novel nomogram provides improved accuracy for predicting biochemical recurrence after radical prostatectomy

The Sensitivity and Specificity of Multiparametric Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography for Predicting Seminal Vesicle Invasion in Clinically Significant Prostate Cancer: A Multicenter Retrospective Study

Background/Objectives: The presence of seminal vesicle invasion (SVI) in prostate cancer (PCa) is associated with poorer postoperative outcomes. This study evaluates the predictive value of magnetic resonance imaging (MRI) and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) for SVI in PCa. Methods: This cohort study included consecutive robotic prostatectomy patients for PCa at three Australian tertiary referral centres between April 2016 and September 2022. MRI and PSMA PET/CT results, clinicopathological variables, including age, BMI, prostate-specific antigen (PSA), PSA density, DRE, Biopsy Gleason score, Positive biopsy cores, PIRADS v2.1 score, MRI volume and MRI lesion size were extracted. The sensitivity, specificity, and accuracy of MRI and PSMA PET/CT for predicting SVI were compared with the histopathological results by receiver operating characteristic (ROC) analysis. Subgroup univariate and multivariate analysis was performed. Results: Of the 528 patients identified, 86 had SVI on final pathology. MRI had a low sensitivity of 0.162 (95% CI: 0.088-0.261) and a high specificity of 0.963 (95% CI: 0.940-0.979). The PSMA PET/CT had a low sensitivity of 0.439 (95% CI: 0.294-0591) and a high specificity of 0.933 (95% CI: 0.849-0.969). When MRI and PSMA PET/CT were used in combination, the sensitivity and specificity improved to 0.514 (95%CI: 0.356-0.670) and 0.880 (95% CI: 0.813-0.931). The multivariate regression showed a higher biopsy Gleason score (p = 0.033), higher PSA (p < 0.001), older age (p = 0.001), and right base lesions (p = 0.003) to be predictors of SVI. Conclusions: MRI and PSMA PET/CT independently underpredicted SVI. The sensitivity and AUC improved when they were used in combination. Multiple clinicopathological factors were associated with SVI on multivariate regression and predictive models incorporating this information may improve oncological outcomes.

Investigation of radiomics models for predicting biochemical recurrence of advanced prostate cancer on pretreatment MR ADC maps based on automatic image segmentation

OBJECTIVES

To develop radiomics models based on automatic segmentation of the pretreatment apparent diffusion coefficient (ADC) maps for predicting the biochemical recurrence (BCR) of advanced prostate cancer (PCa).

METHODS

A total of 100 cases with pathologically confirmed PCa were retrospectively included in this study. These cases were randomly divided into training (n = 70) and test (n = 30) datasets. Two predictive models were constructed based on the combination of age, prostate specific antigen (PSA) level, Gleason score, and clinical staging before therapy and the prostate area (Model_1) or PCa area (Model_2). Another two predictive models were constructed based on only prostate area (Model_3) or PCa area (Model_4). The area under the receiver operating characteristic curve (ROC AUC) and precision-recall (PR) curve analysis were used to analyze the models' performance.

RESULTS

Sixty-five patients without BCR (BCR-) and 35 patients with BCR (BCR+) were confirmed. The age, PSA, volume, diameter and ADC value of the prostate and PCa were not significantly different between the BCR- and BCR+ groups or between the training and test datasets (all p > 0.05). The AUCs were 0.637 (95% CI: 0.434-0.838), 0.841 (95% CI: 0.695-0.940), 0.840 (95% CI: 0.698-0.983), and 0.808 (95% CI: 0.627-0.988) for Model_1 to Model_4 in the test dataset without significant difference. The 95% bootstrap confidence intervals for the areas under the PR curve of the four models were not statistically different.

CONCLUSION

The radiomics models based on automatically segmented prostate and PCa areas on the pretreatment ADC maps developed in our study can be promising in predicting BCR of advanced PCa.

MRI-derived tumor volume as a predictor of biochemical recurrence and adverse pathology in patients after radical prostatectomy: a propensity score matching study

PURPOSE

To investigate the predictive value of MRI-derived tumor volume (TV) of biochemical recurrence (BCR) and adverse pathology (AP) in patients following radical prostatectomy (RP).

METHODS

The data of 565 patients receiving RP in a single institution between 2010 and 2021 were retrospectively analyzed. All suspicious tumor foci were delineated manually using ITK-SNAP software as the regions of interest (ROIs). The sum of the TV of all lesions was calculated automatically based on the voxel in the ROIs to acquire the final TV parameter. TV was categorized as low-volume (≤ 6.5 cm³) and high-volume (> 6.5 cm³) based on the cut-off value. Univariate and multivariate Cox and logistic regression analyses were performed to identify independent predictors of BCR and AP. The Kaplan-Meier with the log-rank test was conducted to compare the BCR-free survival (BFS) between the low and high-volume groups.

RESULTS

All the included patients were divided into the low-volume group (n = 337) and the high-volume group (n = 228). The TV was an independent predictor of BFS in the multivariate Cox regression analysis (Hazard Ratio (HR) [95% CI]: 1.550 [1.066-2.256], P = 0.022). The Kaplan-Meier analysis demonstrated that low volume was associated with a better BFS than high volume before propensity score matching (PSM) (P < 0.001). One hundred and fifty-eight pairs were obtained by 1:1 PSM to balance the baseline parameters between the two groups. After the PSM, low-volume remained to be associated with a better BFS than high-volume (P = 0.006). TV as a categorical variable was an independent factor of AP in multivariate logistic regression analysis (Odd ratio (OR) [95% CI]: 1.821 [1.064-3.115], P = 0.029). After balancing the potential factors influencing AP by 1:1 PSM, 162 new pairs were identified. The high-volume group had a higher AP rate than the low-volume group after PSM (75.9 vs. 64.8%, P = 0.029).

CONCLUSION

We adopted a novel approach to acquiring the TV on preoperative MRI. TV was significantly associated with BFS and AP of patients undergoing RP, which was further illustrated by PSM analysis. MRI-derived TV may serve as a predictive marker for assessing BFS and AP in further studies, which will facilitate clinical decision-making and patient counseling.

Novel nomogram to predict biochemical recurrence-free survival after radical prostatectomy

PURPOSE

Conditional survival represents the probability of subsequent survival given that patients have already survived a certain length of time. Several models predict biochemical recurrence (BCR) after radical prostatectomy. However, none of them include postoperative prostate-specific antigen (PSA). We aimed to analyze BCR-free survival evolution over time and develop a nomogram incorporating the postoperative PSA value to predict BCR-free survival.

MATERIAL AND METHODS

We included patients treated with robot-assisted radical prostatectomy (RARP) for prostate cancer between 2009 and 2021 and calculated conditional survival. Cox proportional hazard regression analysis was used to assess the predictive variables of BCR. We developed a nomogram predicting BCR-free survival three and five years after RARP. We used c-index and decision curve analyses to compare the nomogram with the Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score.

RESULTS

We included 718 patients. The overall 3- and 5-year BCR-free survival rates were 85.1% and 75.7%, respectively. The 5-year BCR-free survival rates increased to 78.9%, 82.9%, 85.2%, and 84.7% for patients surviving 1, 2, 3, and 4 years without BCR, respectively. We developed a nomogram including the pathological Gleason score and T stage, positive surgical margin, PSA ≥ 0.05 ng/mL at one year, and lymph node involvement to predict BCR at 3 and 5 years postoperatively. Our nomogram presented a higher c-index (0.89) than the CAPRA-S score (0.78; p = 0.001) and a positive net benefit at 3 and 5 years postoperatively in the decision curve analyses.

CONCLUSION

The 5-year conditional BCR-free survival increased with survival without BCR. The developed nomogram significantly improved the accuracy in predicting BCR-free survival after RARP.