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Editorial: Retzius‐sparing robot‐assisted radical prostatectomy

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In their commentary in the current issue of BJUI, Stonier et al. [1] examine the potential technical pitfalls and published results of the Retzius‐sparing technique of robotic radical prostatectomy. The authors reviewed three studies from three different groups [2,3], including a study by our group [4], and raised three specific concerns: the oncological efficacy of the procedure; the long learning curve; and the generalizability of the technique to challenging surgical scenarios. We offer a few clarifications and comments.

The first study on Retzius‐sparing robot‐assisted radical prostatectomy came from the Bocciardi group [2]. This was a prospective, single‐arm study of 200 patients. The authors reported a 14‐day continence rate of 90–92%, a 1‐year potency rate of 71–81% (in preoperatively potent patients undergoing bilateral intrafascial nerve‐sparing) and a positive surgical margin rate of 25.5%. The positive surgical margin rate improved in patients with pT2 disease, from 22% to 9% (P = 0.04) over the course of the study (initial 100 vs subsequent 100 patients), while in patients with pT3 disease, it remained stable at ~45%. Lim et al. [3] also noted an improvement in their overall positive surgical margin rate from 20% to 8% when comparing the initial 25 patients with the subsequent 25 patients. In that study, a standard robot‐assisted radical prostatectomy comparator arm was included and there were no differences in overall positive surgical margin rates (14% in both arms), while continence was better with the Retzius‐sparing approach.

Recognizing the potentially technically challenging nature of the Bocciardi approach, we performed a randomized controlled trial to objectively evaluate the technique. Randomized controlled trials are typically designed to answer a single question. Our trial was designed to determine whether there were differences in the rate of return of urinary continence, the primary benefit that previous non‐controlled studies had reported. This our study clearly showed [4].

Once the trial was completed, post hoc analysis of secondary outcomes was performed [5]. One of these outcomes was the positive surgical margin rate. In our trial, we noted an overall positive surgical margin rate of 25% in the Retzius‐sparing arm vs 13% in the control arm, a difference that did not achieve statistical significance (P = 0.11). Stonier et al. [1] suggested that if the sample size of our trial were doubled, then the positive surgical margin rate in each group would be doubled as well, leading to significance. This conclusion is problematic. The likelihood that doubling the sample size would result in the exact doubling of numbers in all four cells of a 2 × 2 contingency table is estimated at <5% using Fisher’s exact test (this calculation is different from the P value). Furthermore, the surgical margins depend as much on the pathological stage as on surgical approach. In our trial, patients were matched preoperatively for risk in the best manner possible for a pragmatic randomized trial. However, it is impossible to predict and control for the final pathological characteristics. Pathological analysis showed that patients undergoing Retzius‐sparing surgery did have significantly more aggressive disease: ≥pT3 disease in 45% vs 23.3% of patients (P = 0.04) [4, 5]. This, by itself, could account for a substantial difference in surgical margin rates.

In writing our paper, we made no judgements as to whether the Bocciardi or posterior technique is fundamentally superior to an anterior or Menon approach, whether it is easier to perform, how generalizable it is [6], or what the learning curve may be. That is best left to the individual surgeon’s training and judgement. We do suggest, however, that surgical margins be interpreted as a function of pathological variables, and not in isolation, and that it is simplistic to assume that identical results will be obtained by doubling sample size. We suggest that such conclusions are hypothesis‐generating, and should best be explored through a separate, purpose‐designed randomized trial.

Authors: Akshay Sood, Firas Abdollah and Mani Menon

References

  1. Stonier T, Simson N, Davis J, Challacombe B. Retzius‐sparing robot‐assisted radical prostatectomy (RS‐RARP) vs standard RARP: it’s time for critical appraisal. BJU Int 2019; 123: 5–10
  2. Galfano A, Di Trapani D, Sozzi F et al. Beyond the learning curve of the Retzius‐sparing approach for robot‐assisted laparoscopic radical prostatectomy: oncologic and functional results of the first 200 patients with >/= 1 year of follow‐up. Eur Urol 2013; 64: 974–80
  3. Lim SK, Kim KH, Shin TY et al. Retzius‐sparing robot‐assisted laparoscopic radical prostatectomy: combining the best of retropubic and perineal approaches. BJU Int 2014; 114: 236–44
  4. Dalela D, Jeong W, Prasad MA et al. A pragmatic randomized controlled trial examining the impact of the Retzius‐sparing approach on early urinary continence recovery after robot‐assisted radical prostatectomy. Eur Urol 2017; 72: 677–85
  5. Menon M, Dalela D, Jamil M et al. Functional recovery, oncologic outcomes and postoperative complications after robot‐assisted radical prostatectomy: an evidence‐based analysis comparing the Retzius sparing and standard approaches. J Urol 2018; 199: 1210–7
  6. Galfano A, Secco S, Bocciardi AM. Will Retzius‐sparing prostatectomy be the future of prostate cancer surgery? Eur Urol 2017; 72: 686–8

 

Editorial: Reply: RS-RARP vs standard RARP

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Since the introduction of robotic surgery in the treatment of patients with prostate cancer (PCa), different surgical innovations have been implemented in order to preserve postoperative functional outcomes while maintaining oncological safety. Sparing the Retzius space during robot‐assisted radical prostatectomy (RARP) was introduced early this decade by Galfano et al [1]. Interestingly, 90% and 96% of patients treated with Retzius‐sparing RARP (RS‐RARP) were continent (no pad/safety pad) at 1 week and 1 year, respectively. Similarly, our group reported a 70% continence rate (no pad) at 1 month after RS‐RARP [2].

The fast urinary continence recovery after RS‐RARP is related to several anatomical factors: the anterior Retzius space is kept intact; the urinary bladder is not dropped; the endopelvic fascia and puboprostatic ligaments are preserved; and there is minimal distortion of the supporting urethral tissues. A recent study reported [3] that less bladder neck descent was observed during postoperative cystogram in patients treated with RS‐RARP than in those treated with standard RARP.

In a recent randomized controlled study, the postoperative continence rate at 1 week was 48% in standard RARP compared with 71% in RS‐RARP (P = 0.01), and this difference was maintained at 3 months (86% standard RARP vs 95% RS‐RARP; P = 0.02). At 1 year, however, the effect on urinary continence difference was muted (93.3% standard RARP vs 98.3% RS‐RARP; P = 0.09) [4]. Similarly, Chang et al. [3] found that the higher continence rate at 1 week (73.3% RS‐RARP vs 26.7% standard RARP; P = 0.000) had vanished at 1 year (100% vs 93.3%; P = 0.15). By contrast, a large recent prospective series showed that the superiority of RS‐RARP in terms of higher early urinary continence was maintained at 1 year (97.5% RS‐RARP vs 68.5% standard RARP) [5].

In addition to a higher early continence rate, RS‐RARP has a lower incidence of postoperative inguinal hernia occurrence compared with standard RARP [6]. Theoretically, RS‐RARP may provide several other potential advantages. It may be advantageous if patients require future surgery necessitating access to the Retzius space and dropping of the bladder, such as an artificial urinary sphincter implantation, an inflatable penile prosthesis insertion, or kidney transplantation. In addition, in patients with previous inguinal hernia repair using mesh, it enables the avoidance of anterior adhesions by accessing the prostate directly from the Douglas pouch. Notably, large‐size glands and/or middle‐lobe, advanced/high‐risk PCa, and patients with previous prostatic surgeries can be managed safely with RS‐RARP in experienced hands.

Undoubtedly, oncological safety is our main concern in treating cancer. To determine the effectiveness of new treatment methods, long‐term follow‐up is warranted. Biochemical recurrence (BCR) is widely used as a primary oncological outcome to assess PCa treatment success. To our knowledge, after radical prostatectomy, ~35% of patients are at risk of developing BCR in the next 10 years. Currently, there are insufficient data regarding the oncological outcomes of RS‐RARP. Only four articles have compared early oncological outcomes between RS‐RARP and standard RARP, and there was no significant difference (Table 1).

More recently, we reported on the mid‐term oncological outcomes of 359 patients who underwent RS‐RARP. The median follow‐up was 26 months. Although this period is not long enough to reach a meaningful conclusion on the oncological safety of RS‐RARP, it is the longest follow‐up period reported in literature. Overall, the positive surgical margin (PSM) rate was 30.6% (14.6% in pT2 and 40.8% in pT3a disease) and the BCR rate was 14.8%. In terms of functional outcomes, the urinary continence rate at 1 year was 93.9% [7]. Interestingly, 164 patients (45.7%) of our cohort had high‐risk PCa. In these patients, the PSM rate was 41.2%, the BCR rate was 22%, and the 3‐year BCR‐free survival (BCRFS) rate was 72%. We compared our results with those in patients with high‐risk PCa treated with standard RARP in the literature. In studies that used the D’Amico criteria the median follow‐up ranged from 12.5 to 37.3 months, the PSM rates were 20.5% to 53.3%, the BCR rates were 17.4% to 31% and the 3‐year BCRFS rates were 41.4% to 86%. In studies that used the National Comprehensive Cancer Network criteria, the median follow‐up ranged from 23.6 to 27 months, the PSM rates were 29% to 38%, the BCR rates were 9.4% to 33%, and the 3‐year BCRFS rates were 55% to 66% [7].

In summary, RS‐RARP is a novel surgical approach which is associated with better urinary continence recovery in the first few months compared with standard RARP [2,3,4,5]. This superiority might be maintained [5] or equalized at 1 year [3,4]. A few studies have compared the early oncological results between RS‐RARP and standard RARP and no significant difference was found [2,3,4,5]. Recently, our group reported the mid‐term oncological outcomes of patients with high‐risk PCa treated with RS‐RARP and these were similar to those of large studies of conventional RARP. This confirms effective and safe mid‐term BCR control after RS‐RARP, while the long‐term oncological results are awaited [7]. Currently, >4 000 cases of RS‐RARP are performed worldwide and more centres are beginning to use and converting to Retzius‐sparing surgery. All centres are experiencing faster recovery of continence. Thanks are due to Drs Galfano and Bocciardi for exploring and sharing this surgical frontier.

 

References

  1. Galfano A, Di Trapani D, Sozzi F, et al. Beyond the learning curve of the Retzius‐sparing approach for robotassisted laparoscopic radical prostatectomy: oncologic and functional results of the first 200 patients with ? 1 year of follow‐up. Eur Urol 2013; 64: 974‐80
  2. Lim SK, Kim KH, Shin TY et al. Retzius‐sparing robot‐assisted laparoscopic radical prostatectomy: combining the best of retropubic and perineal approaches. BJU Int 2014; 114: 236–44
  3. Chang LW, Hung SC, Hu JC et al. Retzius‐sparing robotic‐assisted radical prostatectomy associated with less bladder neck descent and better early continence outcome. Anticancer Res 2018; 38: 345–51
  4. Menon M, Dalela D, Jamil M et al. Functional recovery, oncologic outcomes and postoperative complications after robot‐assisted radical prostatectomy: an evidence‐based analysis comparing the Retzius sparing and standard approaches. J Urol 2018; 199: 1210–7
  5. Sayyid RK, Simpson WG, Lu C et al. Retzius sparing robotic assisted laparoscopic radical prostatectomy: a safe surgical technique with superior continence outcomes. J Endourol 2017; 31: 1244–50
  6. Chang KD, Abdel Raheem A, Santok GDR et al. Anatomical Retzius‐space preservation is associated with lower incidence of postoperative inguinal hernia development after robot‐assisted radical prostatectomy. Hernia 2017; 21: 555–61
  7. Abdel Raheem A, Kidon C, Alenzi M et al. Predictors of biochemical recurrence after retzius‐sparing robot‐assisted radical prostatectomy: analysis of 359 cases with a median follow‐up of 26 months. Int J Urol 2018; 25: 1006–14

 

Resident’s podcast: Retzius‐sparing robot‐assisted radical prostatectomy

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Maria Uloko is a Urology Resident at the University of Minnesota Hospital. In this podcast she discusses the following BJUI Article of the Week:

Retzius‐sparing robot‐assisted radical prostatectomy (RS‐RARP) vs standard RARP: it’s time for critical appraisal

Thomas Stonier*, Nick Simson*, John Davis† and Ben Challacombe‡

 

*Department of Urology, Princess Alexandra Hospital, Harlow, ‡Urology Centre, Guy ’s Hospital, London, UK and †Department of Urology, MD Anderson Cancer Center, Houston, TX, USA

 

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Abstract

Since robot‐assisted radical prostatectomy (RARP) started to be regularly performed in 2001, the procedure has typically followed the original retropubic approach, with incremental technical improvements in an attempt to improve outcomes. These include the running Van‐Velthoven anastomosis, posterior reconstruction or ‘Rocco stitch’, and cold ligation of the Santorini plexus/dorsal vein to maximise urethral length. In 2010, Bocciardi’s team in Milan proposed a novel posterior or ‘Retzius‐sparing’ RARP (RS‐RARP), mirroring the classic open perineal approach. This allows avoidance of supporting structures, such as the puboprostatic ligaments, endopelvic fascia, and Santorini plexus, preserving the normal anatomy as much as possible and limiting damage that may contribute to improved postoperative continence and erectile function. There has been much heralding of the excellent functional outcomes in both the medical and the lay press, but as yet no focus or real mention of any potential downsides of this new technique.

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Article of the Month: Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy

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Every month, the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

If you only have time to read one article this week, it should be this one.

Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy

Nathan C. Wong , Cameron Lam, Lisa Patterson and Bobby Shayegan
Division of Urology, Department of Surgery, McMaster University, Hamilton, ON, Canada

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Visual abstract created Rebecca Fisher @beckybeckyfish

Abstract

Objectives

To train and compare machine‐learning algorithms with traditional regression analysis for the prediction of early biochemical recurrence after robot‐assisted prostatectomy.

Patients and Methods

A prospectively collected dataset of 338 patients who underwent robot‐assisted prostatectomy for localized prostate cancer was examined. We used three supervised machine‐learning algorithms and 19 different training variables (demographic, clinical, imaging and operative data) in a hypothesis‐free manner to build models that could predict patients with biochemical recurrence at 1 year. We also performed traditional Cox regression analysis for comparison.

= 0.686) and with a univariate regression model (AUC = 0.865).

Results

K‐nearest neighbour, logistic regression and random forest classifier were used as machine‐learning models. Classic Cox regression analysis had an area under the curve (AUC) of 0.865 for the prediction of biochemical recurrence. All three of our machine‐learning models (K‐nearest neighbour (AUC 0.903), random forest tree (AUC 0.924) and logistic regression (AUC 0.940) outperformed the conventional statistical regression model. Accuracy prediction scores for K‐nearest neighbour, random forest tree and logistic regression were 0.976, 0.953 and 0.976, respectively.

Conclusions

Machine‐learning techniques can produce accurate disease predictability better that traditional statistical regression. These tools may prove clinically useful for the automated prediction of patients who develop early biochemical recurrence after robot‐assisted prostatectomy. For these patients, appropriate individualized treatment options can improve outcomes and quality of life.

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Editorial: Can machine‐learning algorithms replace conventional statistics?

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Wong et al. [1] evaluate 19 clinical variables (training data) and three supervised machine‐learning algorithms to predict early biochemical recurrence after robot‐assisted prostatectomy. They further compare the areas under the curve (AUCs) resulting from these algorithms with the AUC of a conventional Cox regression model and conclude that the machine‐learning algorithms can produce accurate disease prognosis, perhaps better than a traditional Cox regression model. As the authors state, predictive models have the potential to better individualize care to patients at highest risk of prostate cancer recurrence and progression.

The authors should be commended for their adoption of machine‐learning algorithms to better interpret the vast volumes of clinical data and assess prognosis after robot‐assisted prostatectomy. This should represent another step forward for the management of prostate cancer, where tailored treatment is now largely based on the clinical risk stratification of the disease [2]. Incidentally, we are also in an era where we are seeing aspects of artificial intelligence (machine learning being a subset of it) vastly transform how we view and process data in everyday life. This has been true in medicine as well, particularly for prostate cancer [3].

While our own research group has also evaluated machine‐learning algorithms to process surgeon performance metrics and predict clinical outcomes after robot‐assisted prostatectomy [4], I want to express a word of caution. Utilization of machine learning does not in itself imply automatic superiority over conventional statistics [5] despite literature that has demonstrated so [3]. The success of predictive models in machine learning still relies on the quality of data introduced and careful execution of the analysis. In our experience, it works best when highly experienced clinicians and data scientists are working hand in hand.

Furthermore, I would argue that the results of this present study do not necessarily show that machine learning is superior to conventional statistics, but rather it highlights an inherent advantage of machine learning. While traditional analyses require the a priori selection of a model based on the available data, machine learning has more flexibility for model fitting [6]. Additionally, inclusion of variables in traditional analyses is constrained by the sample size. In contrast, by design, machine learning models thrive on their ability to consider many variables concurrently, and as such, have the potential to detect underlying patterns that may otherwise be undetectable when data are examined effectively in individual silos.

We look forward to the external validation of the methodology described in the present article. Big and diverse data are critical requirements of machine learning. A multi‐institutional, multi‐surgeon cohort is necessary to confirm the findings in this report. A further step from there is the adoption of such prediction models into clinical use. The ultimate question is how improved prognostic data may influence surgeon and patient decisions.

Conflict of Interest

Dr Hung reports personal fees from Ethicon, Inc, outside the submitted work.

References

  1. Wong NC, Lam C, Patterson L, Shayegan B. Use of machine learning to predict early biochemical recurrence following robotic prostatectomy. BJU Int 2019; 123: 51–7
  2. D’Amico AV, Whittington R, Malkowicz SB et al. Biochemical outcome after radical prostatectomy, external beam radiation therapy or interstitial radiation therapy for clinically localized prostate cancer. JAMA 1998; 280: 969–74
  4. Hung AJ, Chen J, Che Z et al. Utilizing machine learning and automated performance metrics to evaluate robot‐assisted radical prostatectomy performance and predict outcomes. J Endourol 2018; 32: 438–445
  5. Kattan MW. Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol 2003; 170 (6 Pt 2): S6–9
  6. Hung AJ, Chen J, Gill IS. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg 2018; 153: 770–1

Infographic: Development of a side‐specific, mpMRI‐based nomogram for the prediction of extracapsular extension of PCa

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Infographic: Development of a side‐specific, mpMRI‐based nomogram for the prediction of extracapsular extension of PCa

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Article of the week: Development of a side‐specific, mpMRI‐based nomogram for the prediction of extracapsular extension of PCa

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Every week, the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there are two accompanying editorials written by prominent members of the urological community. These are intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation. There is also a video produced by the authors. 

If you only have time to read one article this week, it should be this one.

Development and internal validation of a side‐specific, multiparametric magnetic resonance imaging‐based nomogram for the prediction of extracapsular extension of prostate cancer

Alberto Martini*†, Akriti Gupta*, Sara C. Lewis‡, Shivaram Cumarasamy*, Kenneth G. Haines III§, Alberto Briganti†, Francesco Montorsi† and Ashutosh K. Tewari*

 

Departments of *Urology, ‡Radiology, §Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA and †Department of Urology, Vita-Salute San Raffaele University, Milan, Italy
Read the full article

Abstract

Objectives

To develop a nomogram for predicting side‐specific extracapsular extension (ECE) for planning nerve‐sparing radical prostatectomy.

Materials and Methods

We retrospectively analysed data from 561 patients who underwent robot‐assisted radical prostatectomy between February 2014 and October 2015. To develop a side‐specific predictive model, we considered the prostatic lobes separately. Four variables were included: prostate‐specific antigen; highest ipsilateral biopsy Gleason grade; highest ipsilateral percentage core involvement; and ECE on multiparametric magnetic resonance imaging (mpMRI). A multivariable logistic regression analysis was fitted to predict side‐specific ECE. A nomogram was built based on the coefficients of the logit function. Internal validation was performed using ‘leave‐one‐out’ cross‐validation. Calibration was graphically investigated. The decision curve analysis was used to evaluate the net clinical benefit.

Results

The study population consisted of 829 side‐specific cases, after excluding negative biopsy observations (n = 293). ECE was reported on mpMRI and final pathology in 115 (14%) and 142 (17.1%) cases, respectively. Among these, mpMRI was able to predict ECE correctly in 57 (40.1%) cases. All variables in the model except highest percentage core involvement were predictors of ECE (all P ≤ 0.006). All variables were considered for inclusion in the nomogram. After internal validation, the area under the curve was 82.11%. The model demonstrated excellent calibration and improved clinical risk prediction, especially when compared with relying on mpMRI prediction of ECE alone. When retrospectively applying the nomogram‐derived probability, using a 20% threshold for performing nerve‐sparing, nine out of 14 positive surgical margins (PSMs) at the site of ECE resulted above the threshold.

Conclusion

We developed an easy‐to‐use model for the prediction of side‐specific ECE, and hope it serves as a tool for planning nerve‐sparing radical prostatectomy and in the reduction of PSM in future series.

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Editorial: A novel nomogram for predicting ECE of prostate cancer

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We read with great interest the publication on the side‐specific multiparametric magnetic resonance imaging (mpMRI)‐based nomogram from Martini et al. [1].

The prediction of extracapsular extension (ECE) of prostate cancer is of utmost importance to inform accurate surgical planning before radical prostatectomy (RP).

Today, surgical strategy is tailored to the patient’s characteristics, and the need for a correct prediction of ECE is of paramount importance to guarantee oncological safety, as well as optimal functional outcome. The most up‐to‐date guidelines suggest referring to nomograms to decide whether or not to perform nerve‐sparing (NS) surgery. Since the first version of the Partin Tables in 1993, several models have been developed based on PSA, Gleason score at prostate biopsy, and clinical staging, as the most used covariates.

Furthermore, mpMRI is increasingly used in the diagnostic pathway of prostate cancer to aid prostate biopsy targeting and to attain a more accurate diagnosis of clinically significant prostate cancer. Despite its recognised role in the detection of cancer, the accuracy for local staging is poor, providing a low and heterogeneous sensitivity for the detection of ECE [2].

Given this limitation, the addition of MRI to clinically derived nomograms might result in an improved assessment of preoperative local staging. In a retrospective analysis of 501 patients who underwent RP, MRI + clinical models outperformed clinical‐based models alone for all staging outcomes, with better discrimination in predicting ECE with MRI + Partin Tables and MRI + Cancer of the Prostate Risk Assessment (CAPRA) score than nomograms alone [3].

In the current article, Martini et al. [1] suggest a novel nomogram for predicting ECE that includes the presence of a ‘documented definite ECE at mpMRI’ as an additional variable beyond PSA, Gleason score, and maximum percentage of tumour in the biopsy core with the highest Gleason score. Readers should recognise that this is the first model integrating side‐specific MRI findings together with side‐specific biopsy data to provide a ‘MRI‐based side‐specific prediction of ECE’, in an effort to support the surgical decision for a uni‐ or bilateral NS approach.

However, given the frail generalisability of nomograms in different datasets even after external validation [4], a predictive tool has to be built on a rigorous methodology with clear reproducibility of all steps the covariates derive from.

In this respect, the current model raises some concerns.

The schedule of preoperative MRI assessment is arbitrary, with imaging being performed either before (23.9%) or after systematic biopsy (76.1%), and amongst patients with a MRI prior to biopsy, only 94 of 134 patients underwent additional targeted sampling. As a result, MRI is applied by chance in three different ways: before prostate biopsy without targeted sampling, before prostate biopsy with targeted sampling, and after prostate biopsy.

Based upon this heterogeneous MRI timing, the performance of such a model in a novel population may be biased depending on the diagnostic pathway applied at each institution.

The choice of the variables included represents another point of concern. The output of two out of four covariates, ECE depiction at mpMRI and the percentage of tumour in the biopsy core, have been deliberately dichotomised, without taking into account the continuous trend intrinsic to both variables.

Actually, local staging in the European Society of Urogenital Radiology (ESUR) guidelines has been scored on a 1–5 point scale to grade the likelihood of an ECE event. The authors deliberately dichotomised mpMRI findings, considering ‘the loss of prostate capsule and its irregularity’ as suggestive of ECE and ‘broad capsular contact, abutment or bulge without gross ECE’ evocative of organ‐confined disease. As a result, the included MRI covariate may account for a gross prediction of ECE, maintaining the inaccurate and inter‐reader subjective interpretation of local staging intrinsic to MRI.

Beyond those methodological concerns and the moderate sample size that may limit the reproducibility of the model, we wonder if such a prediction really assists the surgeon’s capability to perform a tailored surgery.

The ‘all or none’ era of NS surgery is over, and we are currently able to grade NS according to different approaches reported in the literature. Particularly, Tewari et al. [5] proposed a NS approach based on four grades of dissection, with the veins on the lateral aspect as vascular landmarks to gain the correct dissection planes. Patel et al. [6] described a five‐grade scale of dissection, using the arterial periprostatic vasculature as a landmark to the same purpose.

If we are able to grade a NS surgery, the prediction of ECE should be graded as well and should answer the prerequisite of knowing the amount of prostate cancer extent outside the capsule. How does a surgeon make the decision to follow a more or less conservative dissection otherwise?

We tried to address this issue by using a tool aimed at predicting the amount of ECE [the Predicting ExtraCapsular Extension in Prostate cancer tool] [6] and supporting the choice of the correct plane of dissection with a suggested decision rule. In our study, developed on a large sample of nearly 12 000 prostatic lobes and several combined clinicopathological variables, the absence of imaging characterization was the major point of weakness.

To date, the ideal predictive tool has yet to be described. However, in the modern era of precision surgery, we think that a model should encompass the surgical knowledge and techniques currently available.

Future developments will probably include three‐dimensional surgical navigation models displayed on the TilePro™ function of the robotic console (Intuitive Surgical Inc., Sunnyvale, CA, USA), based on the integration of MRI (for the number, size and location of disease) and predictive tools (to define the amount of ECE).

 

References

  1. Martini A, Gupta A, Lewis SC et al. Development and internal validation of a side‐specific, multiparametric magnetic resonance imaging‐based nomogram for the prediction of extracapsular extension of prostate cancer. BJU Int 2018; 122: 1025–33
  2. de Rooij M, Hamoen EH, Witjes JA, Barentsz JO, Rovers MM. Accuracy of magnetic resonance imaging for local staging of prostate cancer: a diagnostic meta‐analysis. Eur Urol 2016; 70: 233–45
  3. Morlacco A, Sharma V, Viers BR et al. The incremental role of magnetic resonance imaging for prostate cancer staging before radical prostatectomy. Eur Urol 2017; 71: 701–4
  4. Bleeker SE, Moll HA, Steyerberg EW et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol 2003; 56: 826–32
  5. Tewari AK, Srivastava A, Huang MW et al. Anatomical grades of nerve sparing: a risk‐stratified approach to neural‐hammock sparing during robot‐assisted radical prostatectomy (RARP). BJU Int 2011; 108: 984–92
  6. Patel VR, Sandri M, Grasso AA et al. A novel tool for predicting extracapsular extension during graded partial nerve sparing in radical prostatectomy. BJU Int 2018; 121: 373–82

 

Editorial: A picture is worth a thousand words… but does it add utility to a nomogram to predict extraprostatic extension?

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Martini et al. [1] ask whether adding in prostate MRI data to a preoperative nomogram can usefully aid in the decision to nerve‐spare on one or both sides in men undergoing radical prostatectomy, using a dataset of 829 positive prostate lobes in 561 men. The nomogram includes PSA, maximum ipsilateral Gleason grade, percentage core involvement, and presence of extracapsular extension (ECE) on MRI, although the percentage core involvement (< or >50%) was not found to be significant. Pathological ECE was noted in 142 (17.1%) of the lobes, and radiological suspicion of ECE was noted in 115 (14%) lobes.

The incorporation of MRI in the decision‐making process is to be welcomed. However, MRI only correctly predicted ECE in 57/142 (40.1%) cases, showing significant over‐ and under‐detection on MRI criteria alone. Nerve‐sparing was done in 78% of men, and 30 men had a positive surgical margin. The authors found the nomogram to have greater accuracy in predicting ECE than MRI alone, with an area under the curve for MRI alone of 68.83%, compared to 82.92% for the nomogram. The use of the nomogram to inform a decision to nerve‐spare, made independently for each side, is proposed.

We need to be clear about the different definitions that are being applied here. The MRI features used for assessing ECE, namely bulging/irregular margin, obliteration of the rectoprostatic angle, >1 cm capsular abutment, and neurovascular bundle invasion, set a somewhat high threshold, which we would expect to correlate with significant histological burden and ECE. The exact pathological definition of ECE is not described by the authors and so presumably includes presence of any cancer outside the surgical capsule, whilst the presence of a positive surgical margin is defined as any tumour touching an inked margin. This difference in the threshold for radiological and pathological significance of ECE has been noted by others [2]. In addition, there is some discussion of the long‐term clinical significance of a positive surgical margin of <3 mm [3], although both ECE and PSM are recognised as predictors of recurrence.

Even given this discrepancy in definitions, there are other possible reasons why MRI was less predictive than might be expected [4]. The majority (76%) of the MRI scans were done after biopsy, which is known to reduce the accuracy of MRI, resulting in both under‐ and over‐staging. These post‐biopsy effects can persist for some considerable time, often past the 4 week post‐biopsy recovery period used as the minimum in this series, and in many institutions [5]. Differences in prevalence of pathological ECE (17% in this series [1] vs 32.4% in the series reported by Gaunay et al. [4]) could also affect the performance characteristics of MRI for staging.

An alternative to the preoperative nomogram approach is the use of techniques such as neurovascular structure‐adjacent frozen‐section examination (NeuroSAFE) [6]. This allows an intraoperative decision on the extent of excision, based on frozen‐section examination, and it has been shown to increase the ability to nerve‐spare, with associated improved functional outcomes, whilst reducing positive surgical margins. However, it does have significant cost implications and is not widely available.

It makes sense to use preoperative MRI, currently widely recommended for staging, in combination with clinical parameters, to maximise the use of nerve‐sparing to favour functional outcomes, whilst minimising positive surgical margins. Martini et al. [1] present a nomogram based on readily available parameters, which could be readily adopted in the routine setting. The move towards MRI before first biopsy is likely to give us more accurate imaging data, which should help us to further refine the decision to nerve‐spare for men undergoing radical prostatectomy.

References

  1. Martini A, Gupta A, Lewis S et al. Development and internal validation of a side‐specific, multiparametric magnetic resonance imaging‐based nomogram for the prediction of extracapsular extension of prostate cancer. BJU Int 2018; 122: 1025–33
  3. Dev HS, Wiklund P, Patel V et al. Surgical margin length and location affect recurrence rates after robotic prostatectomy. Urol Oncol 2015; 33: 109.e7‐13
  4. Gaunay GS, Patel V, Shah P et al. Multi‐parametric MRI of the prostate: factors predicting extracapsular extension at the time of radical prostatectomy. Asian J Urol 2017; 4: 31–6
  5. Latifoltojar A, Dikaios N, Ridout A et al. Evolution of multi‐parametric MRI quantitative parameters following transrectal ultrasound‐guided biopsy of the prostate. Prostate Cancer Prostatic Dis 2015; 18: 343–51
  6. Mirmilstein G, Rai BP, Gbolahan O et al. The neurovascular structure‐adjacent frozen‐section examination (NeuroSAFE) approach to nerve sparing in robot‐assisted laparoscopic radical prostatectomy in a British setting ‐ a prospective observational comparative study. BJU Int 2018; 121: 854–62

 

 

Video: Development and internal validation of a side‐specific, mpMRI‐based nomogram for the prediction of extracapsular extension of PCa

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Development and internal validation of a side‐specific, multiparametric magnetic resonance imaging‐based nomogram for the prediction of extracapsular extension of prostate cancer

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Abstract

Objectives

To develop a nomogram for predicting side‐specific extracapsular extension (ECE) for planning nerve‐sparing radical prostatectomy.

Materials and Methods

We retrospectively analysed data from 561 patients who underwent robot‐assisted radical prostatectomy between February 2014 and October 2015. To develop a side‐specific predictive model, we considered the prostatic lobes separately. Four variables were included: prostate‐specific antigen; highest ipsilateral biopsy Gleason grade; highest ipsilateral percentage core involvement; and ECE on multiparametric magnetic resonance imaging (mpMRI). A multivariable logistic regression analysis was fitted to predict side‐specific ECE. A nomogram was built based on the coefficients of the logit function. Internal validation was performed using ‘leave‐one‐out’ cross‐validation. Calibration was graphically investigated. The decision curve analysis was used to evaluate the net clinical benefit.

Results

The study population consisted of 829 side‐specific cases, after excluding negative biopsy observations (n = 293). ECE was reported on mpMRI and final pathology in 115 (14%) and 142 (17.1%) cases, respectively. Among these, mpMRI was able to predict ECE correctly in 57 (40.1%) cases. All variables in the model except highest percentage core involvement were predictors of ECE (all P ≤ 0.006). All variables were considered for inclusion in the nomogram. After internal validation, the area under the curve was 82.11%. The model demonstrated excellent calibration and improved clinical risk prediction, especially when compared with relying on mpMRI prediction of ECE alone. When retrospectively applying the nomogram‐derived probability, using a 20% threshold for performing nerve‐sparing, nine out of 14 positive surgical margins (PSMs) at the site of ECE resulted above the threshold.

Conclusion

We developed an easy‐to‐use model for the prediction of side‐specific ECE, and hope it serves as a tool for planning nerve‐sparing radical prostatectomy and in the reduction of PSM in future series.

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