MR-based prognostic nomogram for prostate cancer after radical prostatectomy

Association between mpMRI detected tumor apparent diffusion coefficient and 5-year biochemical recurrence risk after radical prostatectomy

PURPOSE

To assess the ability of tumor apparent diffusion coefficient (ADC) values obtained from multiparametric magnetic resonance imaging (mpMRI) to predict the risk of 5-year biochemical recurrence (BCR) after radical prostatectomy (RP).

MATERIALS AND METHODS

This retrospective analysis included 1207 peripheral and 232 non-peripheral zone prostate cancer (PCa) patients who underwent mpMRI before RP (2012-2015), with the outcome of interest being 5-year BCR. ADC was evaluated as a continuous variable and as categories: low (< 850 µm2/s), intermediate (850-1100 µm2/s), and high (> 1100 µm2/s). Kaplan-Meier curves with log-rank testing of BCR-free survival, multivariable Cox proportional hazard regression models were formed to estimate the risk of BCR.

RESULTS

Among the 1439 males with median age 63 (± 7) years, the median follow-up was 59 months, and 306 (25%) patients experienced BCR. Peripheral zone PCa patients with BCR had lower tumor ADC values than those without BCR (874 versus 1025 µm2/s, p < 0.001). Five-year BCR-free survival rates were 52.3%, 74.4%, and 87% for patients in the low, intermediate, and high ADC value categories, respectively (p < 0.0001). Lower ADC was associated with BCR, both as continuously coded variable (HR: 5.35; p < 0.001) and as ADC categories (intermediate versus high ADC-HR: 1.56, p = 0.017; low vs. high ADC-HR; 2.36, p < 0.001). In the non-peripheral zone PCa patients, no association between ADC and BCR was observed.

CONCLUSION

Tumor ADC values and categories were found to be predictive of the 5-year BCR risk after RP in patients with peripheral zone PCa and may serve as a prognostic biomarker.

MRI characteristics and oncological follow-up of patients with ISUP grade group 4 or 5 prostate cancer

OBJECTIVES

To analyze multiparametric MRI (mpMRI) characteristics of patients with International Society of Urological Pathology (ISUP) grade group (GG) 4 or 5 prostate cancer (PC) and to correlate MRI parameters with the occurrence of biochemical recurrence (BCR) after radical prostatectomy (RPE).

METHODS

In this single-center cohort study consecutive patients with mpMRI and ISUP GG 4 or 5 PC were retrospectively analyzed. Clinical, MR-guided biopsy, and diagnostic mpMRI parameter were assessed. A subcohort of patients with RPE and follow-up was analyzed separately. A univariate and multivariate analyses were performed to determine parameters that are associated to patients with BCR after RPE.

RESULTS

145 patients (mean age 70y, median PSA 10.9 ng/ml) were analyzed. 99% had a PI-RADS classification of 4 or 5, 48% revealed MRI T3 stage, and median diameter of the MRI index lesion (IL) was 15 mm. IL showed a median ADC value of 668 ×10-6 mm2/s and exhibited contrast enhancement in 94% of the cases. For patients with follow-up after RPE (n = 82; mean follow-up time 68 ± 27 m), MRI parameters were significantly different for contact length of the IL to the pseudocapsule (LCC), MRI T3 stage, and IL localization (p < 0.05). Higher PSAD and MRI T3 stage were independent parameters for the risk of BCR when incorporating clinical, biopsy, and MRI parameters.

CONCLUSION

ISUP GG 4 or 5 PC has distinctive characteristics on mpMRI and were detected on MRI in all cases. In addition, higher PSAD and MRI T3 stage were significant predictors for BCR after RPE.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

The Prognostic Value of PI-RADS Score in CyberKnife Ultra-Hypofractionated Radiotherapy for Localized Prostate Cancer

Prostate Imaging-Reporting and Data System (PI-RADS) has been widely implemented as a diagnostic tool for significant prostate cancer (PCa); less is known about its prognostic value, especially in the setting of primary radiotherapy. We aimed to analyze the association between PI-RADS v. 2.1 classification and risk of metastases, based on a group of 152 patients treated with ultra-hypofractionated stereotactic CyberKnife radiotherapy for localized low or intermediate risk-group prostate cancer. We found that all distant failures (n = 5) occurred in patients diagnosed with a PI-RADS score of 5, and axial measurements of the target lesion were associated with the risk of developing metastases (p < 0.001). The best risk stratification model (based on a combination of greatest dimension, the product of multiplication of PI-RADS target lesion axial measurements, and age) achieved a c-index of 0.903 (bootstrap-validated bias-corrected 95% CI: 0.848−0.901). This creates a hypothesis that PI-RADS 5 and the size of the target lesion are important prognostic factors in early-stage PCa patients and should be considered as an adverse prognostic measure for patients undergoing early treatment such as radiation or focal therapy.

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).

Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.

Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.

Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.

Association Between Multiparametric Magnetic Resonance Imaging of the Prostate and Oncological Outcomes after Primary Treatment for Prostate Cancer: A Systematic Review and Meta-analysis

CONTEXT

The diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) diagnosis has been extensively explored. Little is known about the prognostic value of mpMRI suspicion scores and other quantitative mpMRI information.

OBJECTIVE

To systematically review the current literature assessing the relationship between pretreatment mpMRI and oncological outcomes after primary treatment for PCa to assess the role of mpMRI as a prognostic tool.

EVIDENCE ACQUISITION

A computerized bibliographic search of MEDLINE/PubMed, EMBASE, Scopus, and the Cochrane Library CENTRAL databases was performed for all studies assessing the relationship between mpMRI and oncological outcomes after primary treatment for PCa. The review protocol is registered in the PROSPERO database (CRD42020209899).

EVIDENCE SYNTHESIS

A total of six studies were included. Reliable evidence is still limited in this field. The Prostate Imaging-Reporting and Data System (PI-RADS) score was an independent predictor of biochemical recurrence (BCR) after radical prostatectomy (RP) in the majority of the studies included. The tumor volume at mpMRI was not significantly associated with BCR after RP for PCa. Data on disease progression and PCa-specific mortality are limited. Heterogeneity among the studies was substantial.

CONCLUSIONS

The review shows that PI-RADS scores provide information on the future likelihood of cancer recurrence or progression, at least for men undergoing RP. We are of the view that this information should be taken into account to identify men at higher risk of unfavorable outcomes.

PATIENT SUMMARY

A higher Prostate Imaging-Reporting and Data System score for magnetic resonance imaging of the prostate seems to be positively associated with oncological failure in prostate cancer and should be incorporated into future risk models.

Tumor contact length of prostate cancer determined by a three-dimensional method on multiparametric magnetic resonance imaging predicts extraprostatic extension and biochemical recurrence

OBJECTIVE

To evaluate the clinical benefit of tumor contact length as a predictor of pathological extraprostatic extension and biochemical recurrence in patients undergoing prostatectomy.

METHODS

A total of 91 patients who underwent 3T multiparametric magnetic resonance imaging before prostatectomy from April 2014 to July 2019 were included. A total of 94 prostate cancer foci were analyzed retrospectively. We evaluated maximum tumor contact length, which was determined to be the maximum value in the three-dimensional directions, as a predictor of pathological extraprostatic extension and biochemical recurrence.

RESULTS

A total of 19 lesions (20.2%) had positive pathological extraprostatic extension. Areas under the curves showed maximum tumor contact length to be a significantly better parameter to predict pathological extraprostatic extension than the Prostate Imaging Reporting and Data System (P = 0.002), tumor maximal diameter (P = 0.001), prostate-specific antigen (P = 0.020), Gleason score (P < 0.001), and clinical T stage (P < 0.001). Multivariate analysis showed maximum tumor contact length (P = 0.003) to be an independent risk factor for predicting biochemical recurrence. We classified the patients using preoperative factors (prostate-specific antigen >10, Gleason score >3 + 4 and maximum tumor contact length >10 mm) into three groups: (i) high-risk group (patients having all factors); (ii) intermediate-risk group (patients having two of three factors); and (iii) low-risk group (patients having only one or none of the factors). Kaplan-Meier curves showed that the high-risk group had significantly worse biochemical recurrence than the intermediate-risk group (P = 0.042) and low-risk group (P < 0.001).

CONCLUSIONS

Our findings suggest that maximum tumor contact length is an independent predictor of pathological extraprostatic extension and biochemical recurrence. A risk stratification system using prostate-specific antigen, Gleason score and maximum tumor contact length might be useful for preoperative assessment of prostate cancer patients.

The prognostic nomogram including MRI for locally advanced prostate cancer treated by radical prostatectomy

OBJECTIVE

To establish the prognostic nomogram for locally advanced prostate cancer (LAPC) patients treated by radical prostatectomy (RP) based on clinical and multiparametric-MRI (mp-MRI) metrics.

METHODS

One hundred and twenty-one patients diagnosed with LAPC were included in this study. They were all examined by mp-MRI within one week before surgery and treated by RP (36 with RP alone, 48 with neoadjuvant hormonal therapy (NHT) and 37 with neoadjuvant chemohormonal therapy (NCHT)). The biochemical progression-free survival (bPFS) was analyzed by Kaplan-Meier method. Univariate and multivariate analysis were used to determine prognostic factors that were related with bPFS. The prognostic nomogram was established by factors that were significant in multivariate analyses.

RESULTS

The median bPFS had significant difference in the subgroup of treatment (RP alone: 2 [0.00-5.04] vs. NHT: 9.3 [6.746-11.854] vs. NCHT: 11.17 [0.000-25.075] months [Log rank p < .001]), the subgroup of hyperintensity within prostate in DWI (negative: 15.97 [11.202-20.731] vs. positive: 5.2 [2.952-7.448] months [Log rank p < .001]) and the subgroup of pelvic lymph node metastasis (negative: 10.2 [8.404-11.996] vs. unilateral: 4.43 [0.000-11.086] vs. Bilateral: 1.83 [0.636~3.031] [Log rank p < .001]). The method of treatment (hazards ratio [HR], 0.566; 95% confidence interval [CI], 0.356-0.899; p = .016), hyperintensity within prostate in DWI (HR, 2.539; 95% CI, 1.349-4.779; p = .004) and the metastasis burden of pelvic lymph node (HR, 2.492; 95% CI, 1.645-3.777; p < .001) were identified as independent predictors with significance in multivariable Cox regression analysis. The nomogram was established based on these three factors.

CONCLUSION

We established a nomogram based on three significant prognosis factors including the neoadjuvant therapeutic schedule, hyperintensity within prostate in DWI and the metastasis burden of pelvic lymph nodes, which were associated with the clinical outcomes in LAPC patients after surgery.

Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer

Background

Accurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. Here, we built a PLNM-Risk calculator to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND).

Methods

The PLNM-Risk calculator was developed in 280 patients and verified internally in 71 patients and externally in 50 patients by integrating a set of radiologists’ interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep transfer learning algorithms. Its clinical applicability was compared with Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.

Findings

The PLNM-Risk achieved good diagnostic discrimination with areas under the receiver operating characteristic curve (AUCs) of 0.93 (95% CI, 0.90-0.96), 0.92 (95% CI, 0.84-0.97) and 0.76 (95% CI, 0.62-0.87) in the training/validation, internal test and external test cohorts, respectively. If the number of ePLNDs missed was controlled at < 2%, PLNM-Risk provided both a higher number of ePLNDs spared (PLNM-Risk 59.6% vs MSKCC 44.9% vs Briganti 38.9%) and a lower number of false positives (PLNM-Risk 59.3% vs MSKCC 70.1% and Briganti 72.7%). In follow-up, patients stratified by the PLNM-Risk calculator showed significantly different biochemical recurrence rates after surgery.

Interpretation

The PLNM-Risk calculator offers a noninvasive clinical biomarker to predict PLNM for patients with PCa. It shows improved accuracy of diagnosis support and reduced overtreatment burdens for patients with findings suggestive of PCa.

Funding

This work was supported by the Key Research and Development Program of Jiangsu Province (BE2017756) and the Suzhou Science and Technology Bureau-Science and Technology Demonstration Project (SS201808).

Clinical outcomes associated with prostate cancer conspicuity on biparametric and multiparametric MRI: a protocol for a systematic review and meta-analysis of biochemical recurrence following radical prostatectomy

INTRODUCTION

There is an increasing body of evidence to suggest that visibility of prostate cancer on magnetic resonance (MRI) may be related to likelihood of adverse pathological outcomes. Biochemical recurrence (BCR) after radical prostatectomy remains a significant clinical challenge and a means of predicting likelihood of this prior to surgery could inform treatment choice. It appears that MRI could be a potential candidate strategy for BCR prediction, and as such, there is a need to review extant literature on the prognostic capability of MRI. Here, we describe a protocol for a systematic review and meta-analysis of the utility of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) in predicting BCR following radical prostatectomy for prostate cancer treatment.

METHODS AND ANALYSIS

PubMed, MEDLINE, Embase and Cochrane databases will be searched and screening will be guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. In order to meet the inclusion criteria, papers must be English-language articles involving patients who have had bpMRI or mpMRI for suspected prostate cancer and have undergone radical prostatectomy as definitive therapy. Patients must have had prostate-specific antigen monitoring before and after surgery. All relevant papers published from July 1977 to October 2020 will be eligible for inclusion. The Newcastle-Ottawa score will be used to determine the quality and bias of the studies. This protocol is written in-line with the PRISMA protocol 2015 checklist.

ETHICS AND DISSEMINATION

There are no relevant ethical concerns. Dissemination of this protocol will be via peer-reviewed journals as well as national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42020206074.

Prognostic Significance for Long-Term Outcomes Following Radical Prostatectomy in Men with Prostate Cancer: Evaluation with Prostate Imaging Reporting and Data System Version 2

Objective

To retrospectively determine whether the use of the Prostate Imaging Reporting and Data System (PI-RADS) version 2 (v2) helps predict long-term outcomes for prostate cancer (PCa) patients following radical prostatectomy (RP).

Materials and Methods

A total of 166 patients with localized PCa evaluated with multiparametric magnetic resonance imaging (mpMRI) at 3T before RP were enrolled. Three groups were created based on PI-RADS v2 score used to predict clinical outcomes: group A, ≥ 3; group B, ≥ 4; group C, 5. We calculated biochemical recurrence-free survival (RFS) and progression-free survival (PFS). Cox proportion hazards models were used to identify variables predictive of biochemical recurrence and disease progression.

Results

During a median follow-up of 9.1 years, biochemical recurrence occurred in 67 patients (40.4%) and disease progression occurred in 55 patients (33.1%). In all groups, 10-year RFS and 10-year PFS were significantly lower for PI-RADS scores ≥ 3, ≥ 4 and 5 than for score < 3, < 4 and < 5 (p <0.05), respectively. In multivariate analysis, PI-RADS score ≥ 3 and score 5 were significant independent risk marker for biochemical recurrence (hazard ratio [HR] = 5.58, p = 0.018; HR = 1.75, p = 0.033) and disease progression (HR = 3.99, p = 0.047; HR = 2.31, p = 0.040). Moderate inter-observer agreement was seen for PI-RADS scoring.

Conclusion

PI-RADS v2 may be used to predict long-term outcomes following RP in PCa.

The Role of Magnetic Resonance Spectroscopy Imaging Parameters to Predict Early Biochemical Recurrence after Radical Prostatectomy

To evaluate the role of magnetic resonance spectroscopy imaging (MRSI) parameters to predict early biochemical recurrence (BCR) after radical prostatectomy (RP) in patients with non-metastatic prostate cancer (PCa). Between November 2010 and March 2012, 60 consecutive patients with clinically non-metastatic biopsy confirmed PCa underwent RP after MRSI assessment in a prospective study. Demographic, clinicopathological, magnetic resonance imaging (MRI) staging, MRSI parameters, and postoperative serum prostate-specific antigen were recorded. The univariate and multivariate Cox regression analyses were used to assess the association between potential prognosticators and early BCR (BCR less than 12 months after RP). In univariate Cox regression, preoperative serum PSA (prostate-specific antigen) (HR - hazard ratio = 1.016, p=0.003), surgical Gleason score > 7 (HR = 5.034, p=0.006) and MRSI risk score (HR = 4.061, p=0.0001); and in multivariate model, preoperative serum PSA (HR = 1.012; p=0.046), surgical GS > 7 (HR = 4.196; p=0.017) and MRSI risk score (HR = 3.256; p=0.013) were associated with early BCR. The greatest AUC (area under the curve) was related to MRSI risk score (AUC = 0.733) and the AUC of the multivariate model was 0.776. MRI/MRSI parameters specially MRSI risk score might be acceptable predictors of early BCR. These parameters can improve the accuracy of predictive nomograms to assess the risk of BCR after RP.

A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection

OBJECTIVES

To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings.

PATIENTS AND METHODS

In all, 248 patients treated with radical prostatectomy and ePLND or PLND were included. ML-assisted models were developed from 18 integrated features using logistic regression (LR), support vector machine (SVM), and random forests (RFs). The models were compared to the Memorial SloanKettering Cancer Center (MSKCC) nomogram using receiver operating characteristic-derived area under the curve (AUC) calibration plots and decision curve analysis (DCA).

RESULTS

A total of 59/248 (23.8%) lymph node invasions (LNIs) were identified at surgery. The predictive accuracy of the ML-based models, with (+) or without (-) MRI-reported LNI, yielded similar AUCs (RFs⁺ /RFs^- : 0.906/0.885; SVM⁺ /SVM^- : 0.891/0.868; LR⁺ /LR^- : 0.886/0.882) and were higher than the MSKCC nomogram (0.816; P < 0.001). The calibration of the MSKCC nomogram tended to underestimate LNI risk across the entire range of predicted probabilities compared to the ML-assisted models. The DCA showed that the ML-assisted models significantly improved risk prediction at a risk threshold of ≤80% compared to the MSKCC nomogram. If ePLNDs missed was controlled at <3%, both RFs⁺ and RFs^- resulted in a higher positive predictive value (51.4%/49.6% vs 40.3%), similar negative predictive value (97.2%/97.8% vs 97.2%), and higher number of ePLNDs spared (56.9%/54.4% vs 43.9%) compared to the MSKCC nomogram.

CONCLUSIONS

Our ML-based model, with a 5-15% cutoff, is superior to the MSKCC nomogram, sparing ≥50% of ePLNDs with a risk of missing <3% of LNIs.

Prognostic significance of a novel indicator (PSApostd3/PSApre) for PSA recurrence in patients after radical prostatectomy

Purpose

Radical prostatectomy (RP) is a common treatment for prostate cancer, but a fraction of patients may experience PSA recurrence after surgery, manifesting as an elevation in prostate specific antigen (PSA). Vast literature has reported different prognostic factors for PSA recurrence without reaching a consensus. This retrospective study investigated the efficacy of a new indicator in predicting PSA recurrence in patients after RP.

Patients and methods

From October 2000 to December 2015, 102 PCa patients who underwent laparoscopic prostatectomy in the Urology Department of Peking Union Medical College Hospital were analyzed. We calculated PSA_postd3/PSA_pre, defined as the ratio of the PSA on day 3 postop as the numerator and the pre-operative PSA as the denominator, in these patients to represent PSA decrement after surgery, and investigated its relationship with PSA recurrence during follow-up.

Results

The receiver operating characteristic (ROC) curve of PSA_postd3/PSA_pre derived a cut-off at 0.453 (sensitivity=0.704, specificity=0.853, P<0.0001), suggesting an increased risk of PSA recurrence in patients whose PSA on day 3 postop did not decrease to approximately half of their preoperative levels. Among several factors, PSA_postd3/PSA_pre (P<0.0001), pathological T stage (P=0.042) and Gleason Grade (P=0.021) were determined to be significantly associated with PSA recurrence by Fisher’s exact test, while only PSA_postd3/PSA_pre (P<0.001) was significantly related to PSA recurrence-free survival (PRFS) by multivariate logistic regression analysis.

Conclusion

These results imply that PSA_postd3/PSA_pre could provide substantial information for PSA recurrence prediction in patients after RP.

Can We Improve the Preoperative Prediction of Prostate Cancer Recurrence With Multiparametric MRI?

INTRODUCTION

The use of multiparametric magnetic resonance imaging (mpMRI) to assess prostate cancer (PCa) has increased over the past decade. We aimed to assess if preoperative mpMRI lesion score, a variable routinely available for men undergoing pre-biopsy MRI, improves the performance of commonly used preoperative predictive models for PCa recurrence.

PATIENTS AND METHODS

We analyzed data from 372 patients with PCa treated with radical prostatectomy in 2012 to 2017 and assessed with pre-biopsy mpMRI within 6 months prior to surgery. Suspicious areas for cancer were scored on a standardized 5-point scale. Cox regression was used to assess the association between mpMRI score and the risk of postoperative biochemical recurrence. Two different models were tested accounting for factors included in the Kattan nomogram and in the D'Amico risk-classification.

RESULTS

Overall, 53% and 30% of patients were found with a lesion scored 4 or 5 at pre-biopsy mpMRI, respectively. Risk varied widely by mpMRI (29% 2-year risk of biochemical recurrence for a score of 5 vs. 5% for a score of 1-2), and mpMRI score was associated with large hazard ratios after adjusting for stage, grade, and prostate-specific antigen: 1.66, 1.96, and 2.71 for scores 3, 4, and 5, respectively. However, 95% confidence intervals were very wide (0.19-14.20, 0.26-14.65, and 0.36-20.55, respectively) and included 1.

CONCLUSIONS

Our data did not show that preoperative models, commonly used to assess PCa risk, were improved after including the pre-biopsy mpMRI score. However, the value of pre-biopsy mpMRI to improve preoperative risk models should be investigated in larger data sets.

Prostate Imaging-Reporting and Data System Version 2: Beyond Prostate Cancer Detection

The main purpose of Prostate Imaging-Reporting and Data System Version 2 (PI-RADSv2) is to effectively detect clinically significant prostate cancers (csPCa) using multiparametric magnetic resonance imaging. Since the first introduction of PI-RADSv2, researchers have validated its diagnostic performance in identifying csPCa, and these promising data have influenced biopsy and treatment schemes. However, in this article, we focused on the potential of PI-RADSv2 in relation to various aspects of PCa such as Gleason score, tumor volume, extraprostatic extension, lymph node metastasis, and postoperative biochemical recurrence, beyond prostate cancer detection.

Optimising preoperative risk stratification tools for prostate cancer using mpMRI

Purpose

To improve preoperative risk stratification for prostate cancer (PCa) by incorporating multiparametric MRI (mpMRI) features into risk stratification tools for PCa, CAPRA and D’Amico.

Methods

807 consecutive patients operated on by robot-assisted radical prostatectomy at our institution during the period 2010–2015 were followed to identify biochemical recurrence (BCR). 591 patients were eligible for final analysis. We employed stepwise backward likelihood methodology and penalised Cox cross-validation to identify the most significant predictors of BCR including mpMRI features. mpMRI features were then integrated into image-adjusted (IA) risk prediction models and the two risk prediction tools were then evaluated both with and without image adjustment using receiver operating characteristics, survival and decision curve analyses.

Results

37 patients suffered BCR. Apparent diffusion coefficient (ADC) and radiological extraprostatic extension (rEPE) from mpMRI were both significant predictors of BCR. Both IA prediction models reallocated more than 20% of intermediate-risk patients to the low-risk group, reducing their estimated cumulative BCR risk from approximately 5% to 1.1%. Both IA models showed improved prognostic performance with a better separation of the survival curves.

Conclusion

Integrating ADC and rEPE from mpMRI of the prostate into risk stratification tools improves preoperative risk estimation for BCR.

Key points

• MRI-derived features, ADC and EPE, improve risk stratification of biochemical recurrence.

• Using mpMRI to stratify prostate cancer patients improves the differentiation between risk groups.

• Using preoperative mpMRI will help urologists in selecting the most appropriate treatment.

Electronic supplementary material

The online version of this article (10.1007/s00330-017-5031-5) contains supplementary material, which is available to authorized users.

Clinically insignificant prostate cancer suitable for active surveillance according to Prostate Cancer Research International: Active surveillance criteria: Utility of PI-RADS v2

BACKGROUND

Active surveillance (AS) is an important treatment strategy for prostate cancer (PCa). Prostate Imaging-Reporting and Data System (PI-RADS) v2 has been addressed, but few studies have reported the value of PI-RADS v2 for assessing risk stratification in patients with PCa, especially on selecting potential candidates for AS.

PURPOSE

To investigate the utility of PI-RADS v2 and apparent diffusion coefficient (ADC) in evaluating patients with insignificant PCa, who are suitable for AS.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 238 patients with PCa who met the Prostate Cancer Research International: Active Surveillance criteria underwent radical prostatectomy.

FIELD STRENGTH/SEQUENCE

3.0T, including T₂ -weighted, diffusion-weighted, and dynamic contrast-enhanced imaging.

ASSESSMENT

Insignificant cancer was defined histopathologically as an organ-confined disease with a tumor volume <0.5 cm³ without Gleason score 4-5. Patients were divided into two groups based on the PI-RADS v2 and tumor ADC: A, PI-RADS score ≤3 and ADC ≥1.095 × 10^-3 mm² /s; and B, PI-RADS score 4-5 or ADC <1.095 × 10^-3 mm² /s. Preoperative clinical and imaging variables were evaluated regarding the associations with insignificant cancer.

RESULTS

Of the 238 patients, 101 (42.8%) were diagnosed with insignificant cancer on pathological findings. The number of positive cores, prostate-specific antigen density (PSAD), PI-RADS v2 and tumor ADC were significantly associated with insignificant cancer on univariate analysis (P < 0.05). However, multivariate analysis indicated tumor ADC (odds ratio [OR] = 4.57, P < 0.001) and PI-RADS v2 (OR = 3.60, P < 0.001) were independent predictors of insignificant cancer. Area under the receiver operating characteristics curve (AUC) reached 0.803 when PI-RADS v2 (AUC = 0.747) was combined with tumor ADC (AUC = 0.786).

DATA CONCLUSION

The PI-RADS v2 together with tumor ADC may be a useful marker for predicting patients with insignificant PCa when considering AS.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1072-1079.