Index tumor volume on MRI as a predictor of clinical and pathologic outcomes following radical prostatectomy

MRI lesion size is more important than the number of positive biopsy cores in predicting adverse features and recurrence after radical prostatectomy: implications for active surveillance criteria in intermediate-risk patients

INTRODUCTION

To determine associations between prostate cancer (PCa) tumor burden measured on biopsy or multiparametric magnetic resonance imaging (mpMRI) and outcomes in intermediate-risk (IR) International Society of Urological Pathology (ISUP) grade 2 men managed with primary radical prostatectomy (RP).

METHODS

This retrospective, multicenter study was conducted in eight referral centers. The cohort included IR PCa patients who had ISUP 2 at biopsy. We defined biopsy tumor burden as low/high based on the absence/presence of more than 25% positive cores. Tumor burden on imaging was defined as low/high based on maximum lesion diameter, <15 mm and ≥15 mm at mpMRI, respectively. The histological endpoint of the study was adverse features at RP, defined as ≥pT3a stage and/or lymph node invasion and/or ISUP ≥3 at final pathology. The clinical endpoint was biochemical recurrence (BCR) after RP.

RESULTS

A total of 698 IR patients was included, of whom 335 (48%) had adverse features. In multivariate logistic regression analysis, there was no statistical association between tumor burden at biopsy and adverse features (p = 0.7). Tumor size ≥15 mm at mpMRI was significantly associated with adverse pathology (OR 1.65, 95%CI 1.14-2.39; p = 0.01). No significant association was observed between tumor burden at biopsy and BCR (p = 0.4). Tumor size ≥15 mm at mpMRI was significantly associated with BCR (HR 1.96, 95% CI 1.01-3.80; p = 0.04).

CONCLUSIONS

Our data support extending the inclusion criteria to ISUP 2 men with >25% positive cores, provided they have a low tumor size at mpMRI (<15 mm). Prospective studies should be performed to validate these findings.

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

Objectives

To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE).

Methods

Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC).

Results

Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2).

Conclusions

Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).

Quantitative MRI in the Local Staging of Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

Local staging of prostate cancer (PCa) is important for treatment planning. Radiologist interpretation using qualitative criteria is variable with high specificity but low sensitivity. Quantitative methods may be useful in the diagnosis of extracapsular extension (ECE).

PURPOSE

To assess the performance of quantitative MRI markers for detecting ECE.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

4800 patients from 28 studies with histopathologically confirmed PCa on radical prostatectomy were pooled for meta-analysis. Patients from 46 studies were included for systematic review.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted, T2-weighted, and dynamic contrast-enhanced MRI at 1.5 T or 3 T.

ASSESSMENT

PubMed, Embase, Web of Science, Scopus, and Cochrane databases were searched to identify studies on diagnostic test accuracy or association of any quantitative MRI markers with ECE. Results extracted by two independent reviewers for tumor contact length (TCL) and mean apparent diffusion coefficient (ADC-mean) were pooled for meta-analysis, but not for other quantitative markers including radiomics due to low number of studies available.

STATISTICAL TESTS

Hierarchical summary receiver operating characteristic (HSROC) curves were computed for both TCL and ADC-mean, but summary operating points were computed for TCL only. Heterogeneity was investigated by meta-regression. Results were significant if P ≤ 0.05.

RESULTS

At the 10 mm threshold for TCL, summary sensitivity and specificity were 0.76 [95% confidence interval (CI) 0.71-0.81] and 0.68 [95% CI 0.63-0.73], respectively. At the 15 mm threshold, summary sensitivity and specificity were 0.70 [95% CI 0.53-0.83] and 0.74 [95% CI 0.60-0.84] respectively. The area under the HSROC curves for TCL and ADC-mean were 0.79 and 0.78, respectively. Significant sources of heterogeneity for TCL included timing of MRI relative to biopsy.

DATA CONCLUSION

Both 10 mm and 15 mm thresholds for TCL may be reasonable for clinical use. From comparison of the HSROC curves, ADC-mean may be superior to TCL at higher sensitivities.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

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.

3D-Reconstructed Contact Surface Area and Tumour Volume on Magnetic Resonance Imaging Improve the Prediction of Extraprostatic Extension of Prostate Cancer

This study is to determine whether the volume and contact surface area (CSA) of a tumour with an adjacent prostate capsule on MRI in a three-dimensional (3D) model that can predict side-specific extraprostatic extension (EPE) at radical prostatectomy (RP). Patients with localised prostate cancer (PCa) who underwent robot-assisted RP between July 2015 and March 2021 were included in this retrospective study. MRI-based 3D prostate models incorporating the PCa volume and location were reconstructed. The tumour volume and surface variables were extracted. For the prostate-to-tumour and tumour-to-prostate CSAs, the areas in which the distances were ≤ 1, ≤ 2, ≤ 3, ≤ 4, and ≤ 5 mm were defined, and their surface (cm2) were determined. Differences in prostate sides with and without pathological EPE were analysed. Multivariable logistic regression analysis to find independent predictors of EPE. Overall, 75/302 (25%) prostate sides showed pathological EPE. Prostate sides with EPE had higher cT-stage, higher PSA density, higher percentage of positive biopsy cores, higher biopsy Gleason scores, higher radiological tumour stage, larger tumour volumes, larger prostate CSA, and larger tumour CSA (all p < 0.001). Multivariable logistic regression analysis showed that the radiological tumour stage (p = 0.001), tumour volume (p < 0.001), prostate CSA (p < 0.001), and tumour CSA (p ≤ 0.001) were independent predictors of pathological EPE. A 3D reconstruction of tumour locations in the prostate improves prediction of extraprostatic extension. Tumours with a higher 3D-reconstructed volume, a higher surface area of tumour in contact with the prostate capsule, and higher surface area of prostate capsule in contact with the tumour are at increased risk of side-specific extraprostatic extension.

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

Background:

Various prediction tools have been developed to predict biochemical recurrence (BCR) after radical prostatectomy (RP); however, few of the previous prediction tools used serum prostate-specific antigen (PSA) nadir after RP and maximum tumor diameter (MTD) at the same time. In this study, a nomogram incorporating MTD and PSA nadir was developed to predict BCR-free survival (BCRFS).

Methods:

A total of 337 patients who underwent RP between January 2010 and March 2017 were retrospectively enrolled in this study. The maximum diameter of the index lesion was measured on magnetic resonance imaging (MRI). Cox regression analysis was performed to evaluate independent predictors of BCR. A nomogram was subsequently developed for the prediction of BCRFS at 3 and 5 years after RP. Time-dependent receiver operating characteristic (ROC) curve and decision curve analyses were performed to identify the advantage of the new nomogram in comparison with the cancer of the prostate risk assessment post-surgical (CAPRA-S) score.

Results:

A novel nomogram was developed to predict BCR by including PSA nadir, MTD, Gleason score, surgical margin (SM), and seminal vesicle invasion (SVI), considering these variables were significantly associated with BCR in both univariate and multivariate analyses (P < 0.05). In addition, a basic model including Gleason score, SM, and SVI was developed and used as a control to assess the incremental predictive power of the new model. The concordance index of our model was slightly higher than CAPRA-S model (0.76 vs. 0.70, P = 0.02) and it was significantly higher than that of the basic model (0.76 vs. 0.66, P = 0.001). Time-dependent ROC curve and decision curve analyses also demonstrated the advantages of the new nomogram.

Conclusions:

PSA nadir after RP and MTD based on MRI before surgery are independent predictors of BCR. By incorporating PSA nadir and MTD into the conventional predictive model, our newly developed nomogram significantly improved the accuracy in predicting BCRFS after RP.

Extraprostatic extension in prostate cancer: primer for radiologists

The presence of extraprostatic extension (EPE) on multiparametric MRI (mpMRI) is an important factor in determining the management of prostate cancer. EPE is an established risk factor for biochemical recurrence of prostate cancer after radical prostatectomy (RP) and patients with EPE may be considered for wider resection margins, non-nerve-sparing surgery, adjuvant radiation therapy (RT), or androgen deprivation therapy (ADT). Several statistical nomograms and scoring systems have been developed to predict pathological stage at time of RP but with varying accuracies. Using the current PI-RADS v2 mpMRI staging guidelines results in high specificity but lacks in sensitivity. These findings reveal the need for more standardization and further refinement of existing MRI protocols and prostate cancer prediction tools. Current studies have looked into indirect additional imaging criteria such as index tumor volume, length of capsular contact, and apparent diffusion coefficient. Measuring for these features can improve the robustness of mpMRI in staging prostate cancer, as they have been shown to be independent predictors of EPE. MRI/ultrasound fusion-guided targeted biopsy can detect EPE not found on standard biopsy. Collectively, these measurements and imaging techniques can augment the detection of EPE and subsequent risk stratification.

MRI Characteristics Accurately Predict Biochemical Recurrence after Radical Prostatectomy

BACKGROUND

After radical prostatectomy (RP), biochemical recurrence (BCR) is associated with an increased risk of developing distant metastasis and prostate cancer specific and overall mortality.

METHODS

The two-centre study included 521 consecutive patients undergoing RP for positive pre-biopsy magnetic resonance imaging (MRI) and pathologically proven prostate cancer (PCa), after which a combination scheme of fusion-targeted biopsy (TB) and systematic biopsy was performed. We assessed correlations between MRI characteristics, International Society of Urological Pathology (ISUP) grade group in TB, and outcomes after RP. We developed an imaging-based risk classification for improving BCR prediction.

RESULTS

Higher Prostate Imaging and Reporting and Data System (PI-RADS) score (p = 0.013), higher ISUP grade group in TB, and extracapsular extension (ECE) on the MRI were significantly associated with more advanced disease (pTstage), higher ISUP grade group (p = 0.001), regional lymph nodes metastasis in RP specimens (p < 0.001), and an increased risk of recurrence after surgery. A positive margin status was significantly associated with ECE-MRI (p < 0.001). Our imaging-based classification included ECE on MRI, ISUP grade group on TB, and PI-RADS accurately predicted BCR (AUC = 0.714, p < 0.001). This classification had more improved area under the curve (AUC) than the standard d'Amico classification in our population. Validation was performed in a two-centre cohort.

CONCLUSIONS

In this cohort, PI-RADS score, MRI stage, and ISUP grade group in MRI-TB were significantly predictive for disease features and recurrence after RP. Imaging-based risk classification integrating these three factors competed with d'Amico classification for predicting BCR.

Percent tumor volume vs American Joint Committee on Cancer staging system subclassification for predicting biochemical recurrence in patients with pathologic T2 prostate cancer

PURPOSE

Here, we re-checked the American Joint Committee on Cancer 7th edition subclassification and confirmed the possibility of percent tumor volume as a prognostic factor for biochemical recurrence in the 8th edition subclassification.

METHODS

A total of 1073 patients with pathologic T2 stage disease who underwent radical prostatectomy were included. Exclusion criteria were neoadjuvant therapy and pathologic T3 and N1 disease. Biochemical recurrence-free survival was estimated using the Kaplan-Meier method. Cox hazard regression was used to predict biochemical recurrence.

RESULTS

According to the 7th edition subclassification, 141 patients (13.1%) had T2a, 43 (4.0%) had T2b, and 889 (82.9%) had T2c disease. The 7th edition subclassification did not differ significantly on Kaplan-Meier analysis (p = 0.502). Mean percent tumor volume was 8.7 ± 8.0% (interquartile range, 5-10%). Percent tumor volume was positively correlated with initial prostate-specific antigen, grade group, surgical margin, and T2 subclassification (all p < 0.001). The 7th edition subclassification was not a significant factor, whereas percent tumor volume was (hazard ratio, 1.023; 95% confidence interval, 1.005-1.041; p = 0.0128) on multivariate analysis. On Kaplan-Meier analysis, percent tumor volume (> 7.5% vs ≤ 7.5%) differed significantly for biochemical recurrence-free survival (p < 0.001).

CONCLUSIONS

The 7th edition pathologic T2 subclassification had poor prognostic value for biochemical recurrence in our cohort. Elimination of the 8th edition subclassification was suitable. Percent tumor volume classified biochemical recurrence prognosis in pathologic T2 stage. Therefore, percent tumor volume can be a candidate factor for the next T2 subclassification.