Development and External Validation of a Novel Nomogram to Predict Side-specific Extraprostatic Extension in Patients with Prostate Cancer Undergoing Radical Prostatectomy

Utility of 18F-DCFPyL PET for local staging for high or very high risk prostate cancer for patients undergoing radical prostatectomy

PURPOSE

PSMA PET offers the potential for improved local staging prior to radical prostatectomy (RP). We evaluated PSMA PET versus mpMRI for local and nodal staging for patients with localized NCCN high or very high risk prostate cancer undergoing RP.

METHODS

A prospective single center pilot study was conducted from 9/2018 to 6/2022. Patients underwent both mpMRI and 18F-DCFPyL PSMA PET with limited MRI sequences, followed by RP with pelvic lymphadenectomy. Patient and side-specific performance of mpMRI and PSMA PET were compared to RP histopathological standard of truth for extraprostatic extension (EPE), seminal vesicle invasion (SVI), and lymph node involvement (LNI).

RESULTS

At RP, 79% (38/48) had EPE, 31% had SVI, and 31% had LNI. At the patient level for EPE, PSMA PET had similar sensitivity (65.8% vs. 84.2%, respectively, P = 0.07) but higher specificity (80% vs. 40%, P = 0.045) compared to mpMRI, respectively. For SVI, PSMA PET had lower sensitivity (62.5% vs. 87.5%, P = 0.046) and similar specificity (87.5% vs. 90.6%, P = 0.56). For side-specific LNI, PSMA PET had higher sensitivity (50% vs. 25%, P = 0.03) and similar specificity (96.1% vs. 94.7%, P = 0.71) compared to mpMRI.

CONCLUSION

PSMA PET offers higher specificity for EPE and higher sensitivity for LNI compared to mpMRI. PSMA PET may improve overall surgical planning, and may be combined with diagnostic mpMRI and clinicopathological variables through nomograms to further predict EPE, SVI, or LNI.

CLINICAL TRIAL REGISTRATION

NCT03392181 https://clinicaltrials.gov/study/NCT03392181 .

External Validation of the Nomogram Predicting Locally Advanced Prostate Cancer in Patients Undergoing Robot-Assisted Radical Prostatectomy (the MSUG94 Group)

PURPOSE

A clinically applicable nomogram predicting locally advanced prostate cancer (PCa) (defined as pathological T stage ≥ 3) of patients with clinical T stage ≤ 2 was released (MSUG nomogram). We performed external validation, ensuring its applicability to patients undergoing robot-assisted radical prostatectomy (RARP). Therefore, we also compared the external validation for the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram.

PATIENTS AND METHODS

We collected the data for 8194 patients who underwent RARP at Daimonji Clinical Application Database Group (Dai-CAD) as the validation cohort and performed the external validation using this cohort. The primary endpoint was the accuracy of the MSUG nomogram, and the secondary endpoint was comparison with the MSKCC nomogram. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were calculated to quantify the accuracy of the nomogram at predicting pT ≥ 3. A calibration plot was performed to evaluate the extent of over- and underestimation.

RESULTS

Locally advanced PCa was diagnosed in 677 of 2530 patients (26.8%) in the MSUG cohort and 1472 of 5799 patients (25.3%) in the validation cohort. The ROC curve for the validation cohorts fit to the MSUG nomogram and the MSKCC nomogram, with AUC of 0.66 and 0.65, respectively. For calibration plots, it overestimated the risk of locally advanced PCa when probability thresholds are over 70% in the MSUG nomogram, while it may overestimate when probability thresholds are over 30% in the MSKCC nomogram.

CONCLUSIONS

We conducted external validation of a clinically applicable nomogram that predicts the probability of locally advanced PCa in patients undergoing RARP using available clinical parameters.

Prostate magnetic resonance imaging to predict grade concordance, extra prostatic extension, and biochemical recurrence after radical prostatectomy

OBJECTIVES

To investigate whether preoperative prostate MRI findings predicted biopsy to radical prostate (RP) grade group concordance, presence of extraprostatic extension (EPE), and biochemical recurrence (BCR) after RP.

MATERIAL AND METHODS

We conducted a multi-institutional study (tertiary academic center and community practice) including patients who underwent RP (2014-2021) with preoperative MRI. Grade concordance for systematic, targeted, and combined prostate biopsy was compared to RP. Concordances were also compared for a contemporaneous RP cohort without prebiopsy MRI (No MRI cohort). We assessed association of extracapsular extension on MRI (MRI-ECE) with EPE and BCR after RP.

RESULTS

Among 768 men, concordance between biopsy and RP was 65.7% for combined, 58.3% for targeted, and 44.7% for systematic biopsy (P < 0.001). There was no difference in upgrading, concordance, and downgrading compared to 1014 men in the No MRI cohort (P = 0.6). Combined biopsy decreased upgrading to Grade Group ≥3 by 9.2%. EPE after RP was present in 292/768 (38%). MRI-ECE had 56% sensitivity, 74% specificity, 57% positive predictive value, and 73% negative predictive value. MRI-ECE was associated with EPE (OR: 2.25, P < 0.001) and BCR (HR: 1.77, P = 0.006). An MRI-based model improved EPE prediction in the development cohort (AUC 0.80) compared to a traditional nomogram but failed external validation (AUC 0.68).

CONCLUSIONS

Preoperative MRI findings predicted grade concordance, presence of EPE, and risk of BCR after RP. Variability in MRI-ECE interpretation limited generalizability of models to predict EPE indicating a need for more standardized reporting to increase clinical utility.

Clinical value of contralateral biopsies in men with unilateral MRI foci undergoing targeted biopsy

OBJECTIVES

To evaluate the additional prostate cancer detection yield and clinical implications of performing contralateral systematic biopsies in men with unilateral suspicious magnetic resonance imaging (MRI) findings undergoing MRI-guided transperineal (TP) biopsies in an outpatient clinic.

PATIENTS AND METHODS

A prospective study of 655 consecutive men with unilateral MRI suspicious findings undergoing office-based MRI-guided TP biopsies between May 2022 and December 2023. All men had pre-biopsy MRI followed by MRI-guided TP fusion biopsies with at least four targeted cores per lesion plus five contralateral systematic biopsies. The primary objective was the clinically significant prostate cancer (csPCa) detection rate of contralateral systematic biopsies in men with no or insignificant PCa on targeted biopsies. Secondary objectives included the impact of contralateral biopsies on PCa grade upgrading, additional insignificant PCa diagnoses, and the clinical implications of multifocal csPCa detected on both targeted and contralateral cores.

RESULTS

Any and csPCa (Gleason Grade Group [GG] ≥2) was detected in 564/655 (85%) and 471/655 (71%) men with a median age of 66 years and PSA level of 7.6 ng/mL. Overall, seven of 655 (1%) men had csPCa detected by contralateral systematic biopsies missed on MRI-targeted biopsy, all of whom had low-volume Gleason GG 2 PCa eligible for active surveillance. Furthermore, 70/464 (15%) men with csPCa on MRI-targeted biopsy had matching Gleason GG 2-5 PCa on contralateral biopsy, and another seven had higher Gleason GG. However, the additional information from contralateral biopsies did not seem to influence whole-gland treatment allocation and nerve-sparing during surgery.

CONCLUSION

Contralateral systematic biopsies in men with unilateral MRI findings undergoing MRI-guided TP targeted biopsies have limited value for csPCa detection and risk assessment for whole-gland treatment but may be important for determining PCa multifocality considering focal therapy eligibility.

Prognostic significance of the mEPE score in intermediate-risk prostate cancer patients undergoing ultrahypofractionated robotic SBRT

PURPOSE

This study aimed to evaluate the prognostic significance of magnetic resonance imaging (MRI) parameters on biochemical failure-free survival (BFS) in patients diagnosed with intermediate-risk prostate cancer and treated with robotic ultrahypofractionated stereotactic body radiotherapy (SBRT) without androgen deprivation therapy (ADT).

METHODS

A retrospective analysis was conducted in patients with intermediate-risk prostate cancer undergoing robotic SBRT delivered in five fractions with a total radiation dose of 35-36.25 Gy. The primary endpoint was biochemical failure as defined by the Phoenix criteria. Among other clinicopathological data, T stage, Prostate Imaging-Reporting and Data System (PI-RADS) score, and multiparametric magnetic resonance imaging-based extra-prostatic extension (mEPE) score were collected and analyzed using the log-rank test.

RESULTS

A total of 74 patients were eligible for analysis. Median age at treatment was 68.8 years and median prostate volume was 47.8 cm3. Fifty-four and 14 patients were diagnosed with Gleason scores 7a and 7b, respectively. In total, 40 patients were classified as having unfavorable intermediate-risk prostate cancer according to American Urological Association/American Society for Radiation Oncology/ Society of Urologic Oncology (AUA/ASTRO/SUO) guidelines. The median follow-up was 30 months (range: 4-91.2 months; interquartile range (IQR): 18.5-48 months). The 3‑year BFS was 92%. A total of 12 (16.2%) biochemical failures were reported. In univariate analysis, an mEPE score of 5, the delivered total radiation dose (35 Gy vs. 36.25 Gy), and a prostate-specific antigen (PSA) nadir >1 ng/ml were associated with lower BFS (mEPE-BFS: p < 0.001, total radiation dose-BFS: p = 0.04, PSA nadir-BFS: p =< 0.001).

CONCLUSION

Patients diagnosed with intermediate-risk prostate cancer with a high mEPE score are more likely to experience biochemical failure after SBRT. Treatment intensification measures, such as administration of concomitant ADT, should be considered.

Predictors of Contralateral Disease in Men With Unilateral Lesions on Multiparametric Magnetic Resonance Imaging

OBJECTIVE

To evaluate predictors of contralateral clinically significant prostate cancer (csPCa) in men with biopsy-proven unilateral lesions on magnetic resonance imaging (MRI).

METHODS

We retrospectively identified men with no prior diagnosis of PCa with unilateral biopsy-confirmed csPCa within PI-RADS 2-5 lesions within our institutional biopsy database. Multivariate logistic regression was used to identify clinical predictors of contralateral disease.

RESULTS

Four hundred ninety men met study inclusion criteria, of which 385 men (78.6%) had no contralateral csPCa and 105 men (21.4%) had contralateral csPCa (Fig. 1). Prior negative biopsy (OR 0.34 [0.14, 0.75], P = .012), prostate-specific antigen density (OR 18.8 [2.77, 249], P = .017), and tumor location in the transverse plane ("Posterior": OR 1.93 [1.02, 3.87], P = .048; "Throughout Transverse Plane": OR 6.56 [2.26, 19.6], P < .001) were significantly associated with contralateral csPCa in multivariate logistic regression models. However, there appear to be no attributes within the MRI-targeted tumor that reliably predict contralateral csPCa (Table 2).

CONCLUSION

Approximately 20% of men with unilateral MRI findings and csPCa on targeted biopsy were found to have contralateral csPCa on systematic biopsy (SB). Prior negative biopsy was associated with a decreased odds of contralateral csPCa. Prostate-specific antigen density and tumor in the posterior aspect of or throughout the transverse plane were associated with increased odds of contralateral csPCA. Consideration of these clinical factors may afford an opportunity to only use SB in cases in which the odds of contralateral csPCa are high.

External validation of nomograms including MRI features for the prediction of side-specific extraprostatic extension

BACKGROUND

Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP). Multiple nomograms, which include magnetic resonance imaging (MRI) information, are available predict side-specific EPE. It is crucial that the accuracy of these nomograms is assessed with external validation to ensure they can be used in clinical practice to support medical decision-making.

METHODS

Data of prostate cancer (PCa) patients that underwent robot-assisted RP (RARP) from 2017 to 2021 at four European tertiary referral centers were collected retrospectively. Four previously developed nomograms for the prediction of side-specific EPE were identified and externally validated. Discrimination (area under the curve [AUC]), calibration and net benefit of four nomograms were assessed. To assess the strongest predictor among the MRI features included in all nomograms, we evaluated their association with side-specific EPE using multivariate regression analysis and Akaike Information Criterion (AIC).

RESULTS

This study involved 773 patients with a total of 1546 prostate lobes. EPE was found in 338 (22%) lobes. The AUCs of the models predicting EPE ranged from 72.2% (95% CI 69.1-72.3%) (Wibmer) to 75.5% (95% CI 72.5-78.5%) (Nyarangi-Dix). The nomogram with the highest AUC varied across the cohorts. The Soeterik, Nyarangi-Dix, and Martini nomograms demonstrated fair to good calibration for clinically most relevant thresholds between 5 and 30%. In contrast, the Wibmer nomogram showed substantial overestimation of EPE risk for thresholds above 25%. The Nyarangi-Dix nomogram demonstrated a higher net benefit for risk thresholds between 20 and 30% when compared to the other three nomograms. Of all MRI features, the European Society of Urogenital Radiology score and tumor capsule contact length showed the highest AUCs and lowest AIC.

CONCLUSION

The Nyarangi-Dix, Martini and Soeterik nomograms resulted in accurate EPE prediction and are therefore suitable to support medical decision-making.

Utility of positive core number on MRI-ultrasound fusion targeted biopsy in combination with PI-RADS scores for predicting unexpected extracapsular extension of clinically localized prostate cancer

OBJECTIVES

To evaluate the utility of magnetic resonance imaging (MRI) and MRI-ultrasound fusion targeted biopsy (TB) for predicting unexpected extracapsular extension (ECE) in clinically localized prostate cancer (CLPC).

METHODS

This study enrolled 89 prostate cancer patients with one or more lesions showing a Prostate Imaging-Reporting and Data System (PI-RADS) score ≥3 but without morphological abnormality in the prostatic capsule on pre-biopsy MRI. All patients underwent TB and systematic biopsy followed by radical prostatectomy (RP). Each lesion was examined by 3-core TB, taking cores from each third of the lesion. The preoperative variables predictive of ECE were explored by referring to RP specimens in the lesion-based analysis.

RESULTS

Overall, 186 lesions, including 81 (43.5%), 73 (39.2%), and 32 (17.2%) with PI-RADS 3, 4, and 5, respectively, were analyzed. One hundred and twenty-two lesions (65.6%) were diagnosed as cancer on TB, and ECE was identified in 33 (17.7%) on the RP specimens. The positive TB core number was ≤2 in 129 lesions (69.4%) and three in 57 lesions (30.6%). On the multivariate analysis, PI-RADS ≥4 (p = 0.049, odds ratio [OR] = 2.39) and three positive cores on TB (p = 0.005, OR = 3.07) were independent predictors of ECE. Lesions with PI-RADS ≥4 and a positive TB core number of 3 had a significantly higher rate of ECE than those with PI-RADS 3 and a positive TB core number ≤2 (37.5% vs. 7.8%, p < 0.001).

CONCLUSIONS

Positive TB core number in combination with PI-RADS scores is helpful to predict unexpected ECE in CLPC.

What the urologist needs to know before radical prostatectomy: MRI effective support to pre-surgery planning

BACKGROUND

Radical prostatectomy (RP) is recommended in case of localized or locally advanced prostate cancer (PCa), but it can lead to side effects, including urinary incontinence (UI) and erectile dysfunction (ED). Magnetic resonance imaging (MRI) is recommended for PCa diagnosis and staging, but it can also improve preoperative risk-stratification.

PURPOSE

This nonsystematic review aims to provide an overview on factors involved in RP side effects, highlighting anatomical and pathological aspects that could be included in a structured report.

EVIDENCE SYNTHESIS

Considering UI evaluation, MR can investigate membranous urethra length (MUL), prostate volume, the urethral sphincter complex, and the presence of prostate median lobe. Longer MUL measurement based on MRI is linked to a higher likelihood of achieving continence restoration. For ED assessment, MRI and diffusion tensor imaging identify the neurovascular bundle and they can aid in surgery planning. Finally, MRI can precisely describe extra-prostatic extension, prostate apex characteristics and lymph-node involvement, providing valuable preoperative information for PCa treatment.

CONCLUSIONS

Anatomical principals structures involved in RP side effects can be assessed with MR. A standardized MR report detailing these structures could assist urologists in planning optimal and tailored surgical techniques, reducing complications, and improving patients' care.

The Impact of Omitting Contralateral Systematic Biopsy on the Surgical Planning of Patients with a Unilateral Suspicious Lesion on Magnetic Resonance Imaging Undergoing Robot-assisted Radical Prostatectomy for Prostate Cancer

Background and objective

A combined approach of magnetic resonance imaging (MRI)-targeted biopsy (TBx) and bilateral systematic biopsy (SBx) is advised in patients who have an increased risk of prostate cancer (PCa). The diagnostic gain of SBx in detecting PCa for treatment planning of patients undergoing robot-assisted radical prostatectomy (RARP) is unknown. This study aims to determine the impact of omitting contralateral SBx on the surgical planning of patients undergoing RARP in terms of nerve-sparing surgery (NSS) and extended pelvic lymph node dissection (ePLND).

Methods

Case files from 80 men with biopsy-proven PCa were studied. All men had a unilateral suspicious lesion on MRI, and underwent TBx and bilateral SBx. Case files were presented to five urologists for the surgical planning of RARP. Each case file was presented randomly using two different sets of information: (1) results of TBx + bilateral SBx, and (2) results of TBx + ipsilateral SBx. The urologists assessed whether they would perform NSS and/or ePLND.

Key findings and limitations

A change in the surgical plan concerning NSS on the contralateral side was observed in 9.0% (95% confidence interval [CI] 6.4-12.2) of cases. Additionally, the indication for ePLND changed in 5.3% (95% CI 3.3-7.9) of cases. Interobserver agreement based on Fleiss' kappa changed from 0.44 to 0.15 for the indication of NSS and from 0.84 to 0.83 for the indication of ePLND.

Conclusions and clinical implications

In our series, the diagnostic information obtained from contralateral SBx has limited impact on the surgical planning of patients with a unilateral suspicious lesion on MRI scheduled to undergo RARP.

Patient summary

In patients with one-sided prostate cancer on magnetic resonance imaging, omitting biopsies on the other side rarely changed the surgical plan with respect to nerve-sparing surgery and the indication to perform extended lymph node dissection.

Predicting contralateral extraprostatic extension in unilateral high-risk prostate cancer: a multicentric external validation study

PURPOSE

Accurate prediction of extraprostatic extension (EPE) is crucial for decision-making in radical prostatectomy (RP), especially in nerve-sparing strategies. Martini et al. introduced a three-tier algorithm for predicting contralateral EPE in unilateral high-risk prostate cancer (PCa). The aim of the study is to externally validate this model in a multicentric European cohort of patients.

METHODS

The data from 208 unilateral high-risk PCa patients diagnosed through magnetic resonance imaging (MRI)-targeted and systematic biopsies, treated with RP between January 2016 and November 2021 at eight referral centers were collected. The evaluation of model performance involved measures such as discrimination (AUC), calibration, and decision-curve analysis (DCA) following TRIPOD guidelines. In addition, a comparison was made with two established multivariable logistic regression models predicting the risk of side specific EPE for assessment purposes.

RESULTS

Overall, 38%, 48%, and 14% of patients were categorized as low, intermediate, and high-risk groups according to Martini et al.'s model, respectively. At final pathology, EPE on the contralateral prostatic lobe occurred in 6.3%, 12%, and 34% of patients in the respective risk groups. The algorithm demonstrated acceptable discrimination (AUC 0.68), comparable to other multivariable logistic regression models (p = 0.3), adequate calibration and the highest net benefit in DCA. The limitations include the modest sample size, retrospective design, and lack of central revision.

CONCLUSION

Our findings endorse the algorithm's commendable performance, supporting its utility in guiding treatment decisions for unilateral high-risk PCa patients.

Magnetic resonance imaging targeted biopsy in biopsy-naïve patients and the risk of overtreatment in prostate cancer: a grading issue

OBJECTIVE

To evaluate the impact of applying the 2014 and 2019 International Society of Urological Pathology (ISUP) recommendations on grade group distribution and concordance with radical prostatectomy (RP).

MATERIALS AND METHODS

Overall, 655 biopsy-naïve patients diagnosed by magnetic resonance imaging (MRI) targeted and systematic biopsies for Prostate Imaging Reporting and Data System score ≥3 lesions were identified from a prospectively maintained database from 2016 and 2022. Clinically significant prostate cancer was detected in 249 patients, of whom 69 underwent RP. Wilcoxon signed rank and McNemar's tests were used to compare the ISUP grade group distribution and concordance with RP after applying the 2014 (i.e., highest grade) and 2019 (i.e., global grade) ISUP recommendations, respectively.

RESULTS

Compared to the 2014 ISUP recommendations, the 2019 ISUP recommendations were associated with a significant decrease in ISUP Grade Group 4 (range of difference from -13% to -5%) and an increase in ISUP Grade Group 2 (range of difference from +6% to +11%) in MRI targeted biopsy only, MRI targeted with perilesional biopsies, and MRI targeted with systematic biopsies (all P < 0.01). In patients who underwent RP, a significant decrease in downgrading was observed with all biopsy strategies (range of difference from -19% to -12%; P ≤ 0.008), along with an increase in concordance with RP specimen (range of difference from +12% to +13%; P ≤ 0.02). The use of the 2019 ISUP recommendation was associated with RP specimen a lower treatment burden.

CONCLUSIONS

The use of the 2019 ISUP recommendations mitigates the grade migration induced by MRI targeted biopsy and improves the concordance with the final RP specimen.

Development and validation of a predictive model based on clinical and MpMRI findings to reduce additional systematic prostate biopsy

OBJECTIVES

To develop and validate a predictive model based on clinical features and multiparametric magnetic resonance imaging (mpMRI) to reduce unnecessary systematic biopsies (SBs) in biopsy-naïve patients with suspected prostate cancer (PCa).

METHODS

A total of 274 patients who underwent combined cognitive MRI-targeted biopsy (MRTB) with SB were retrospectively enrolled and temporally split into development (n = 201) and validation (n = 73) cohorts. Multivariable logistic regression analyses were used to determine independent predictors of clinically significant PCa (csPCa) on cognitive MRTB, and the clinical, MRI, and combined models were established respectively. Area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses were assessed.

RESULTS

Prostate imaging data and reporting system (PI-RADS) score, index lesion (IL) on the peripheral zone, age, and prostate-specific antigen density (PSAD) were independent predictors and included in the combined model. The combined model achieved the best discrimination (AUC 0.88) as compared to both the MRI model incorporated by PI-RADS score, IL level, and zone (AUC 0.86) and the clinical model incorporated by age and PSAD (AUC 0.70). The combined model also showed good calibration and enabled great net benefit. Applying the combined model as a reference for performing MRTB alone with a cutoff of 60% would reduce 43.8% of additional SB, while missing 2.9% csPCa.

CONCLUSIONS

The combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naïve patients.

CRITICAL RELEVANCE STATEMENT

The combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naïve patients.

KEY POINTS

• Age, PSAD, PI-RADS score, and peripheral index lesion were independent predictors of csPCa. • Risk models were used to predict the probability of detecting csPCa on cognitive MRTB. • The combined model might reduce 43.8% of unnecessary SBs, while missing 2.9% csPCa.

Surgical Management and Considerations for Patients with Localized High-Risk Prostate Cancer

OPINION STATEMENT

Localized high-risk (HR) prostate cancer (PCa) is a heterogenous disease state with a wide range of presentations and outcomes. Historically, non-surgical management with radiotherapy and androgen deprivation therapy was the treatment option of choice. However, surgical resection with radical prostatectomy (RP) and pelvic lymph node dissection (PLND) is increasingly utilized as a primary treatment modality for patients with HRPCa. Recent studies have demonstrated that surgery is an equivalent treatment option in select patients with the potential to avoid the side effects from androgen deprivation therapy and radiotherapy combined. Advances in imaging techniques and biomarkers have also improved staging and patient selection for surgical resection. Advances in robotic surgical technology grant surgeons various techniques to perform RP, even in patients with HR disease, which can reduce the morbidity of the procedure without sacrificing oncologic outcomes. Clinical trials are not only being performed to assess the safety and oncologic outcomes of these surgical techniques, but to also evaluate the role of surgical resection as a part of a multimodal treatment plan. Further research is needed to determine the ideal role of surgery to potentially provide a more personalized and tailored treatment plan for patients with localized HR PCa.

Head-to-Head Comparison of 18F-PSMA-1007 Positron Emission Tomography/Computed Tomography and Multiparametric Magnetic Resonance Imaging with Whole-mount Histopathology as Reference in Localisation and Staging of Primary Prostate Cancer

BACKGROUND

Accurate local staging is critical for treatment planning and prognosis in prostate cancer (PCa). Although multiparametric magnetic resonance imaging (mpMRI) has high specificity for detection of extraprostatic extension (EPE) and seminal vesicle invasion (SVI), its sensitivity remains limited. 18F-PSMA-1007 positron emission tomography/computed tomography (PET/CT) may be more accurate in determining T stage.

OBJECTIVE

To assess the diagnostic performance of 18F-PSMA-1007 PET/CT in comparison to mpMRI for intraprostatic tumour localisation and detection of EPE and SVI in men with primary PCa undergoing robot-assisted radical prostatectomy (RARP).

DESIGN, SETTING, AND PARTICIPANTS

Between February 2019 and October 2020, 105 treatment-naïve patients with biopsy-proven intermediate- or high-risk PCa undergoing mpMRI and 18F-PSMA-1007 PET/CT before RARP were prospectively enrolled.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The diagnostic accuracy of 18F-PSMA-1007 PET/CT and mpMRI for intraprostatic tumour localisation and detection of EPE and SVI was assessed via histopathological examination of whole-mount RP specimens. The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy were calculated. The McNemar test was used to compare outcomes between imaging modalities.

RESULTS AND LIMITATIONS

In 80 RP specimens, 129 PCa lesions were found, of which 96 were clinically significant PCa (csPCa). Per-lesion sensitivity for localisation of overall PCa was 85% (95% confidence interval [CI] 77-90%) with PSMA PET/CT and 62% (95% CI 53-70%) with mpMRI (p < 0.001). Per-lesion sensitivity for csPCa was 95% (95% CI 88-98%) with PSMA PET/CT and 73% (95% CI 63-81%) with mpMRI (p < 0.001). The diagnostic accuracy of PSMA PET/CT and mpMRI for detection of EPE per lesion did not significantly differ (sensitivity 45%, 95% CI 31-60% vs 55%, 95% CI 40-69%; p = 0.3; specificity 85%, 95% CI 75-92% vs 90%, 95% CI 81-86%; p = 0.5). The sensitivity and specificity of PSMA PET/CT and mpMRI for detection of SVI did not significantly differ (sensitivity 47%, 95% CI 21-73% vs 33%, 95% CI 12-62; p = 0.6; specificity 94%, 95% CI 88-98% vs 96%, 95% CI 90-99%; p = 0.8).

CONCLUSIONS

18F-PSMA-1007 is a promising imaging modality for localising intraprostatic csPCa but did not show additional value in assessing EPE and SVI in comparison to mpMRI.

PATIENT SUMMARY

A new imaging technique called PET/CT (positron emission tomography/computed tomography) with the radioactive tracer 18F-PSMA-1007 shows promise in identifying the location of clinically significant prostate cancer. However, it does not seem to be of additional value over magnetic resonance imaging (MRI) for determining the local tumour stage.

Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy

BACKGROUND

The aim of the study was to investigate the predictive value of the nutritional risk index (NRI) for extracapsular extension (ECE) and seminal vesicle invasion (SVI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP), and further develop and validate predictive nomograms for ECE and SVI based on the NRI.

METHODS

We retrospectively analyzed 734 PCa patients who underwent RP between 2010 and 2020 in the Department of Urology at Peking University Third Hospital. The enrolled patients were randomly divided into a primary cohort (n = 489) and a validation cohort (n = 245) in a 2:1 manner. The baseline NRI of patients was calculated using serum albumin level and body mass index, and a malnutrition status was defined as NRI ≤ 98. Univariate and multivariate logistic regression analyses were conducted to identify predictors for ECE and SVI. Nomograms for predicting ECE and SVI were established based on the results of the multivariate logistic regression analysis. The performance of the nomograms was estimated using Harrell's concordance index (C-index), the area under curve (AUC) of receiver operating characteristic (ROC) curves and the calibration curves.

RESULTS

In the primary cohort, 70 (14.3%) patients with NRI ≤ 98 were classified as malnutrition, while the remaining 419 (85.7%) patients with NRI > 98 were considered to have normal nutrition. The nomograms for predicting ECE and SVI shared common factors including NRI, percentage of positive biopsy cores (PPC) and biopsy Gleason score, while prostate-specific antigen (PSA) levels and PSA density (PSAD) were only incorporated in ECE nomogram. The C-indexes of the nomograms for predicting ECE and SVI were 0.785 (95% confidence interval (CI): 0.745 - 0.826) and 0.852 (95% CI: 0.806 - 0.898), respectively. The calibration curves demonstrated excellent agreement between the predictions by the nomograms and the actual observations. The results remained reproducible when the nomograms were applied to the validation cohort.

CONCLUSIONS

The NRI is significantly associated with ECE and SVI in PCa patients. The nomogram established based on the NRI in our study can provide individualized risk estimation for ECE and SVI in PCa patients, and may be valuable for clinicians in making well-informed decisions regarding treatment strategies and patient management.

Nomogram Predicting Locally Advanced Prostate Cancer in Patients with Clinically Organ-Confined Disease Who Underwent Robot-Assisted Radical Prostatectomy: A Retrospective Multicenter Cohort Study in Japan (The MSUG94 Group)

PURPOSE

We created a clinically applicable nomogram to predict locally advanced prostate cancer using preoperative parameters and performed external validation using an external independent validation cohort.

PATIENTS AND METHODS

From a retrospective multicenter cohort study of 3622 Japanese patients with prostate cancer who underwent robot-assisted radical prostatectomy at ten institutions, the patients were divided into two groups (MSUG cohort and validation cohort). Locally advanced prostate cancer was defined as pathological T stage ≥ 3a. A multivariable logistic regression model was used to identify factors strongly associated with locally advanced prostate cancer. Bootstrap area under the curve was calculated to assess the internal validity of the prediction model. A nomogram was created as a practical application of the prediction model, and a web application was released to predict the probability of locally advanced prostate cancer.

RESULTS

A total of 2530 and 427 patients in the MSUG and validation cohorts, respectively, met the criteria for this study. On multivariable analysis, initial prostate-specific antigen, prostate volume, number of cancer-positive and cancer-negative biopsy cores, biopsy grade group, and clinical T stage were independent predictors of locally advanced prostate cancer. The nomogram predicting locally advanced prostate cancer was demonstrated (area under the curve 0.72). Using a nomogram cutoff of 0.26, 464 of 1162 patients (39.9%) could be correctly diagnosed with pT3, and 2311 of 2524 patients (91.6%) could avoid underdiagnosis.

CONCLUSIONS

We developed a clinically applicable nomogram with external validation to predict the probability of locally advanced prostate cancer in patients undergoing robot-assisted radical prostatectomy.

Comprehensive analysis of predictive factors for upstaging in intraprostatic cancer after radical prostatectomy: Different patterns of spread exist in lesions at different locations

BACKGROUND

Accurate assessment of the clinical staging is crucial for determining the need for radical prostatectomy (RP) in prostate cancer (PCa). However, the current methods for PCa staging may yield incorrect results. This study aimed to comprehensively analyze independent predictors of postoperative upstaging of intraprostatic cancer.

METHODS

We conducted a retrospective analysis of data from intraprostatic cancer patients who underwent radical surgery between March 2019 and December 2022. Intraprostatic cancer was defined as a lesion confined to the prostate, excluding cases where multiparameter magnetic resonance imaging (mpMRI) showed the lesion in contact with the prostatic capsule. We assessed independent predictors of extraprostatic extension (EPE) and analyzed their association with positive surgical margin (PSM) status. In addition, based on the distance of the lesion from the capsule on mpMRI, we divided the patients into non-transition zone and transition zone groups for further analysis.

RESULTS

A total of 500 patients were included in our study. Logistic regression analysis revealed that biopsy Gleason grade group (GG) (odds ratio, OR: 1.370, 95% confidence interval, CI: 1.093-1.718) and perineural invasion (PNI) (OR: 2.746, 95% CI: 1.420-5.309) were predictive factors for postoperative EPE. Both biopsy GG and PNI were associated with lateral (GG: OR: 1.270, 95% CI: 1.074-1.501; PNI: OR: 2.733, 95% CI: 1.521-4.911) and basal (GG: OR: 1.491, 95% CI: 1.194-1.862; PNI: OR: 3.730, 95% CI: 1.929-7.214) PSM but not with apex PSM (GG: OR: 1.176, 95% CI: 0.989-1.399; PNI: OR: 1.204, 95% CI: 0.609-2.381) after RP. Finally, PNI was an independent predictor of EPE in the transition zone (OR: 11.235, 95% CI: 2.779-45.428) but not in the non-transition zone (OR: 1.942, 95% CI: 0.920-4.098).

CONCLUSION

PNI and higher GG may indicate upstaging of tumors in patients with intraprostatic carcinoma. These two factors are associated with PSM in locations other than the apex of the prostate. Importantly, cancer in the transition zone of the prostate is more likely to spread externally through nerve invasion than cancer in the non-transition zone.

Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer

RATIONALE AND OBJECTIVES

To develop and evaluate a peritumoral radiomic-based machine learning model to differentiate low-Gleason grade group (L-GGG) and high-GGG (H-GGG) prostate lesions.

MATERIALS AND METHODS

In this retrospective study, a total of 175 patients with prostate cancer (PCa) confirmed by puncture biopsy were recruited and included 59 patients with L-GGG and 116 patients with H-GGG. The original PCa regions of interest (ROIs) were delineated on T2-weighted (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, and then centra-tumoral and peritumoral ROIs were defined. Features were meticulously extracted from each ROI to establish radiomics models, employing distinct sequence datasets. Peritumoral radiomics models were specifically developed for both the peripheral zone (PZ) and transitional zone (TZ), utilizing dedicated PZ and TZ datasets, respectively. The performances of the models were evaluated by using the receiver operating characteristic (ROC) curve and precision-recall curve.

RESULTS

The classification model with combined peritumoral features based on T2 + DWI + ADC sequence dataset demonstrated superior performance compared to the original tumor and centra-tumoral classification models. It achieved an area under the ROC curve (AUC) of 0.850 [95% confidence interval, 0.849, 0.860] and an average accuracy of 0.950. The combined peritumoral model outperformed the regional peritumoral models with AUC of 0.85 versus 0.75 for PZ lesions and 0.88 versus 0.69 for TZ lesions, respectively. The peritumoral classification models exhibit greater efficacy in predicting PZ lesions as opposed to TZ lesions.

CONCLUSION

The peritumoral radiomics features showed excellent performance in predicting GGG in PCa patients and might be a valuable addition to the non-invasive assessment of PCa aggressiveness.

Biopsy prostate cancer perineural invasion and tumour load are associated with positive posterolateral margins at radical prostatectomy: implications for planning of nerve-sparing surgery

AIMS

Radical prostatectomy (RP) for prostate cancer is frequently complicated by erectile dysfunction and urinary incontinence. However, sparing of the nerve bundles adjacent to the posterolateral sides of the prostate reduces the number of complications at the risk of positive surgical margins. Preoperative selection of men eligible for safe, nerve-sparing surgery is therefore needed. Our aim was to identify pathological factors associated with positive posterolateral surgical margins in men undergoing bilateral nerve-sparing RP.

METHODS AND RESULTS

Prostate cancer patients undergoing RP with standardised intra-operative surgical margin assessment according to the NeuroSAFE technique were included. Preoperative biopsies were reviewed for grade group (GG), cribriform and/or intraductal carcinoma (CR/IDC), perineural invasion (PNI), cumulative tumour length and extraprostatic extension (EPE). Of 624 included patients, 573 (91.8%) received NeuroSAFE bilaterally and 51 (8.2%) unilaterally, resulting in a total of 1197 intraoperative posterolateral surgical margin assessments. Side-specific biopsy findings were correlated to ipsilateral NeuroSAFE outcome. Higher biopsy GG, CR/IDC, PNI, EPE, number of positive biopsies and cumulative tumour length were all associated with positive posterolateral margins. In multivariable bivariate logistic regression, ipsilateral PNI [odds ratio (OR) = 2.98, 95% confidence interval (CI) = 1.62-5.48; P < 0.001] and percentage of positive cores (OR = 1.18, 95% CI = 1.08-1.29; P < 0.001) were significant predictors for a positive posterolateral margin, while GG and CR/IDC were not.

CONCLUSIONS

Ipsilateral PNI and percentage of positive cores were significant predictors for a positive posterolateral surgical margin at RP. Biopsy PNI and tumour volume can therefore support clinical decision-making on the level of nerve-sparing surgery in prostate cancer patients.

MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review

PURPOSE

Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature.

METHODS

We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores.

RESULTS

We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science.

CONCLUSION

Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.

Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study

BACKGROUND

Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatment-related side-effects such as impotence and incontinence in patients with localised prostate cancer. Artificial intelligence (AI) might provide robust and personalised ssEPE predictions to better inform nerve-sparing strategy during radical prostatectomy. We aimed to develop, externally validate, and perform an algorithmic audit of an AI-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA).

METHODS

Each prostatic lobe was treated as an individual case such that each patient contributed two cases to the overall cohort. SEPERA was trained on 1022 cases from a community hospital network (Trillium Health Partners; Mississauga, ON, Canada) between 2010 and 2020. Subsequently, SEPERA was externally validated on 3914 cases across three academic centres: Princess Margaret Cancer Centre (Toronto, ON, Canada) from 2008 to 2020; L'Institut Mutualiste Montsouris (Paris, France) from 2010 to 2020; and Jules Bordet Institute (Brussels, Belgium) from 2015 to 2020. Model performance was characterised by area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), calibration, and net benefit. SEPERA was compared against contemporary nomograms (ie, Sayyid nomogram, Soeterik nomogram [non-MRI and MRI]), as well as a separate logistic regression model using the same variables included in SEPERA. An algorithmic audit was performed to assess model bias and identify common patient characteristics among predictive errors.

FINDINGS

Overall, 2468 patients comprising 4936 cases (ie, prostatic lobes) were included in this study. SEPERA was well calibrated and had the best performance across all validation cohorts (pooled AUROC of 0·77 [95% CI 0·75-0·78] and pooled AUPRC of 0·61 [0·58-0·63]). In patients with pathological ssEPE despite benign ipsilateral biopsies, SEPERA correctly predicted ssEPE in 72 (68%) of 106 cases compared with the other models (47 [44%] in the logistic regression model, none in the Sayyid model, 13 [12%] in the Soeterik non-MRI model, and five [5%] in the Soeterik MRI model). SEPERA had higher net benefit than the other models to predict ssEPE, enabling more patients to safely undergo nerve-sparing. In the algorithmic audit, no evidence of model bias was observed, with no significant difference in AUROC when stratified by race, biopsy year, age, biopsy type (systematic only vs systematic and MRI-targeted biopsy), biopsy location (academic vs community), and D'Amico risk group. According to the audit, the most common errors were false positives, particularly for older patients with high-risk disease. No aggressive tumours (ie, grade >2 or high-risk disease) were found among false negatives.

INTERPRETATION

We demonstrated the accuracy, safety, and generalisability of using SEPERA to personalise nerve-sparing approaches during radical prostatectomy.

FUNDING

None.

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.

Pre-operative prediction of extracapsular extension of prostate cancer: first external validation of the PRECE model on an independent dataset

INTRODUCTION

The PRECE is a model predicting the risk of extracapsular extension (ECE) of prostate cancer: it has been developed on more than 6000 patients who underwent robotic radical prostatectomy (RARP) at the Global Robotic Institute, FL, USA. Up to now, it is the single tool predicting either the side and the amount of ECE. The model has a free user-friendly interface and is made up from simple and available covariates, namely age, PSA, cT, GS and percent of positive core, the latter topographically distributed within the prostate gland. Despite the successful performance at internal validation, the model is still lacking an external validation (EV). The aim of the paper is to externally validate the PRECE model on an Italian cohort of patients elected to RARP.

METHODS

269 prostatic lobes from 141 patients represented the validation dataset. The EV was performed with the receiver operating characteristics (ROC) curves and calibration, to address the ability of PRECE to discriminate between patients with or without ECE.

RESULTS

Overall, an ECE was found in 91 out of the 269 prostatic lobes (34%). Twenty-five patients out of pT3 had a bilateral ECE. The ROC curve showed an AUC of 0.80 (95% CI 0.74-0.85). Sensitivity and specificity were 77% and 69%, respectively. The model showed an acceptable calibration with tendency towards overestimation.

CONCLUSIONS

From the current EV, the PRECE displays a good predictive performance to discriminate between cases with and without ECE; despite preliminary, outcomes may support the generalizability of the model in dataset other than the development one.

Side-specific, Microultrasound-based Nomogram for the Prediction of Extracapsular Extension in Prostate Cancer

Background

Prediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario.

Objective

To create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing.

Design setting and participants

Prospective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis.

Outcome measurements and statistical analysis

ECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility.

Results and limitations

The areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement.

Conclusions

We developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results.

Patient summary

The integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.

External Validation of Models for Prediction of Side-specific Extracapsular Extension in Prostate Cancer Patients Undergoing Radical Prostatectomy

BACKGROUND

Predicting the risk of side-specific extracapsular extension (ECE) is essential for planning nerve-sparing radical prostatectomy (RP) in patients with prostate cancer (PCa).

OBJECTIVE

To externally validate available models for prediction of ECE.

DESIGN, SETTING, AND PARTICIPANTS

Sixteen models were assessed in a cohort of 737 consecutive PCa patients diagnosed via multiparametric magnetic resonance imaging (MRI)-targeted and systematic biopsies and treated with RP between January 2016 and November 2021 at eight referral centers.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Model performance was evaluated in terms of discrimination using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS AND LIMITATIONS

Overall, ECE was identified in 308/1474 (21%) prostatic lobes. Prostatic lobes with ECE had higher side-specific clinical stage on digital rectal examination and MRI, number of positive biopsy cores, and International Society of Urological Pathology grade group in comparison to those without ECE (all p < 0.0001). Less optimistic performance was observed in comparison to previous published studies, although the models described by Pak, Patel, Martini, and Soeterik achieved the highest accuracy (AUC ranging from 0.73 to 0.77), adequate calibration for a probability threshold <40%, and the highest net benefit for a probability threshold >8% on DCA. Inclusion of MRI-targeted biopsy data and MRI information in models improved patient selection and clinical usefulness. Using model-derived cutoffs suggested by their authors, approximately 15% of positive surgical margins could have been avoided. Some available models were not included because of missing data, which constitutes a limitation of the study.

CONCLUSIONS

We report an external validation of models predicting ECE and identified the four with the best performance. These models should be applied for preoperative planning and patient counseling.

PATIENT SUMMARY

We validated several tools for predicting extension of prostate cancer outside the prostate gland. These tools can improve patient selection for surgery that spares nerves affecting recovery of sexual potency after removal of the prostate. They could potentially reduce the risk of finding cancer cells at the edge of specimens taken for pathology, a finding that suggests that not all of the cancer has been removed.

Explainable artificial intelligence to predict the risk of side-specific extraprostatic extension in pre-prostatectomy patients

INTRODUCTION

We aimed to develop an explainable machine learning (ML) model to predict side-specific extraprostatic extension (ssEPE) to identify patients who can safely undergo nerve-sparing radical prostatectomy using preoperative clinicopathological variables.

METHODS

A retrospective sample of clinicopathological data from 900 prostatic lobes at our institution was used as the training cohort. Primary outcome was the presence of ssEPE. The baseline model for comparison had the highest performance out of current biopsy-derived predictive models for ssEPE. A separate logistic regression (LR) model was built using the same variables as the ML model. All models were externally validated using a testing cohort of 122 lobes from another institution. Models were assessed by area under receiver-operating-characteristic curve (AUROC), precision-recall curve (AUPRC), calibration, and decision curve analysis. Model predictions were explained using SHapley Additive exPlanations. This tool was deployed as a publicly available web application.

RESULTS

Incidence of ssEPE in the training and testing cohorts were 30.7 and 41.8%, respectively. The ML model achieved AUROC 0.81 (LR 0.78, baseline 0.74) and AUPRC 0.69 (LR 0.64, baseline 0.59) on the training cohort. On the testing cohort, the ML model achieved AUROC 0.81 (LR 0.76, baseline 0.75) and AUPRC 0.78 (LR 0.75, baseline 0.70). The ML model was explainable, well-calibrated, and achieved the highest net benefit for clinically relevant cutoffs of 10-30%.

CONCLUSIONS

We developed a user-friendly application that enables physicians without prior ML experience to assess ssEPE risk and understand factors driving these predictions to aid surgical planning and patient counselling (https://share.streamlit.io/jcckwong/ssepe/main/ssEPE_V2.py).

Negative mpMRI Rules Out Extra-Prostatic Extension in Prostate Cancer before Robot-Assisted Radical Prostatectomy

Background: The accuracy of multi-parametric MRI (mpMRI) in the pre-operative staging of prostate cancer (PCa) remains controversial. Objective: The purpose of this study was to evaluate the ability of mpMRI to accurately predict PCa extra-prostatic extension (EPE) on a side-specific basis using a risk-stratified 5-point Likert scale. This study also aimed to assess the influence of mpMRI scan quality on diagnostic accuracy. Patients and Methods: We included 124 men who underwent robot-assisted RP (RARP) as part of the NeuroSAFE PROOF study at our centre. Three radiologists retrospectively reviewed mpMRI blinded to RP pathology and assigned a Likert score (1-5) for EPE on each side of the prostate. Each scan was also ascribed a Prostate Imaging Quality (PI-QUAL) score for assessing the quality of the mpMRI scan, where 1 represents the poorest and 5 represents the best diagnostic quality. Outcome measurements and statistical analyses: Diagnostic performance is presented for the binary classification of EPE, including 95% confidence intervals and the area under the receiver operating characteristic curve (AUC). Results: A total of 231 lobes from 121 men (mean age 56.9 years) were evaluated. Of these, 39 men (32.2%), or 43 lobes (18.6%), had EPE. A Likert score ≥3 had a sensitivity (SE), specificity (SP), NPV, and PPV of 90.4%, 52.3%, 96%, and 29.9%, respectively, and the AUC was 0.82 (95% CI: 0.77-0.86). The AUC was 0.76 (95% CI: 0.64-0.88), 0.78 (0.72-0.84), and 0.92 (0.88-0.96) for biparametric scans, PI-QUAL 1-3, and PI-QUAL 4-5 scans, respectively. Conclusions: MRI can be used effectively by genitourinary radiologists to rule out EPE and help inform surgical planning for men undergoing RARP. EPE prediction was more reliable when the MRI scan was (a) multi-parametric and (b) of a higher image quality according to the PI-QUAL scoring system.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Nomograms in Urologic Oncology: Lights and Shadows

Decision-making in urologic oncology involves integrating multiple clinical data to provide an answer to the needs of a single patient. Although the practice of medicine has always been an “art” involving experience, clinical data, scientific evidence and judgment, the creation of specialties and subspecialties has multiplied the challenges faced every day by physicians. In the last decades, with the field of urologic oncology becoming more and more complex, there has been a rise in tools capable of compounding several pieces of information and supporting clinical judgment and experience when approaching a difficult decision. The vast majority of these tools provide a risk of a certain event based on various information integrated in a mathematical model. Specifically, most decision-making tools in the field of urologic focus on the preoperative or postoperative phase and provide a prognostic or predictive risk assessment based on the available clinical and pathological data. More recently, imaging and genomic features started to be incorporated in these models in order to improve their accuracy. Genomic classifiers, look-up tables, regression trees, risk-stratification tools and nomograms are all examples of this effort. Nomograms are by far the most frequently used in clinical practice, but are also among the most controversial of these tools. This critical, narrative review will focus on the use, diffusion and limitations of nomograms in the field of urologic oncology.