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Article of the month: Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

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 editorial written by a prominent member of the urology community and a video prepared by the authors; 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.

Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

Francesco Porpiglia*, Daniele Amparore*, Enrico Checcucci*, Matteo Manfredi*, Ilaria Stura, Giuseppe Migliaretti, Riccardo Autorino, Vincenzo Ficarra§ and Cristian Fiori*

 

*Division of Urology, Department of Oncology, School of Medicine, San Luigi Hospital, Department of Public Health and Paediatric Sciences, School of Medicine, University of Turin, Orbassano (Turin), Italy, Division of Urology, VCU Health, Richmond, VA, USA, and §Urological Section, Department of Human and Paediatric Pathology, University of Messina, Messina, Italy

 

Abstract

Objectives

To apply the standard PADUA and RENAL nephrometry score variables to three‐dimensional (3D) virtual models (VMs) produced from standard bi‐dimensional imaging, thereby creating 3D‐based (PADUA and RENAL) nephrometry scores/categories for the reclassification of the surgical complexity of renal masses, and to compare the new 3D nephrometry score/category with the standard 2D‐based nephrometry score/category, in order to evaluate their predictive role for postoperative complications.

Materials and Methods

All patients with localized renal tumours scheduled for minimally invasive partial nephrectomy (PN) between September 2016 and September 2018 underwent 3D and 2D nephrometry score/category assessments preoperatively. After nephrometry score/category evaluation, all the patients underwent surgery. Chi‐squared tests were used to evaluate the individual patients’ grouping on the basis of the imaging tool (3D VMs and 2D imaging) used to assess the nephrometry score/category, while Cohen’s κ coefficient was used to test the concordance between classifications. Receiver‐operating characteristic curves were produced to evaluate the sensitivity and specificity of the 3D nephrometry score/category vs the 2D nephrometry score/category in predicting the occurrence of postoperative complications. A general linear model was used to perform multivariable analyses to identify predictors of overall and major postoperative complications.

Results

A total of 101 patients were included in the study. The evaluation of PADUA and RENAL nephrometry scores via 3D VMs showed a downgrading in comparison with the same scores evaluated with 2D imaging in 48.5% and 52.4% of the cases. Similar results were obtained for nephrometry categories (29.7% and 30.7% for PADUA risk and RENAL complexity categories, respectively). The 3D nephrometry score/category demonstrated better accuracy than the 2D nephrometry score/category in predicting overall and major postoperative complications (differences in areas under the curve for each nephrometry score/category were statistically significant comparing the 3D VMs with 2D imaging assessment). Multivariable analyses confirmed 3D PADUA/RENAL nephrometry category as the only independent predictors of overall (P = 0.007; P = 0.003) and major postoperative complications (P = 0.03; P = 0.003).

Conclusions

In the present study, we showed that 3D VMs were more precise than 2D standard imaging in evaluating the surgical complexity of renal masses according to nephrometry score/category. This was attributable to a better perception of tumour depth and its relationships with intrarenal structures using the 3D VM, as confirmed by the higher accuracy of the 3D VM in predicting postoperative complications.

Editorial: Will three‐dimensional models change the way nephrometric scoring is carried out?

There has been an increase in the extent to which imaging is used for preoperative planning of complex urological procedures. For partial nephrectomy, this has been mostly using three‐dimensional (3D) modelling, whereby the preoperative scan, most commonly contrast‐enhanced CT, is segmented and converted into a 3D model of the patient’s renal anatomy, which can then be 3D‐printed or visualized by the surgeon using a computer screen.

In this issue of BJUI, Porpiglia et al. [1] propose the use of 3D models, visualized using a computer for preoperative nephrometric scoring (PADUA and RENAL) of 101 patients to predict postoperative complications. In this preliminary study, they compare the visual scores obtained by two urologists when evaluating only a 3D model, against the scores of two urologists obtained when evaluating only CT images. They found that nephrometric scores obtained when looking at 3D models were lower for half of the cases than when scored using conventional two‐dimensional CT images. Furthermore, they show that for the 101 patients the scores obtained using 3D information were able to give an improved prediction of postoperative complications. The reason for the improved prediction of postoperative complications using 3D modelling is attributed to a better perception of tumour depth and its relationships with intrarenal structures. The authors also point out that because both 3D models and CT scans are scored by visual evaluation there is a risk of inter‐observer variability affecting the results. Overall, this paper introduces an exciting new topic of research in using advanced image analysis techniques for nephrometric scoring.

Many further opportunities exist for developing these ideas of using quantitative image analysis to improve planning and scoring for partial nephrectomy. Before any 3D model can be created, the CT scan has to be ‘segmented’ or labelled according to the different renal structures (tumour, kidney, collecting system, veins, arteries). Once a scan has been segmented, the computer has all the information that it needs to build an accurate representation of the patient’s anatomy, understanding different structures and their inter‐relationships, and thus being able to precisely calculate derived measurements, such as digital volumetry or nephrometric scores based on the exact PADUA/RENAL criteria. Furthermore, novel and more complex nephrometric scores that use segmentation map descriptors could be developed and fitted to postoperative data to further improve predictions. Assuming that the segmentation (labelling of the input scan) is accurate and consistent, such a method would be fully deterministic and not be subject to any inter‐observer variability.

Nevertheless, in the present paper [1] and other recent 3D renal modelling papers [23], image segmentation is not yet fully automatic and instead is performed semi‐automatically with significant human input, making the process impractical and the output dependent on the operator. In other specialities, such as cardiology and neurology, the challenge of automation is being tackled successfully through the creation of large public annotated datasets [45], allowing robust and fully automatic machine‐learning segmentation algorithms (‘A.I.’) to be developed [4]. The creation of a multi‐institutional open‐source dataset of annotated renal CT scans would pave the way for increased research and progress towards automatic, reliable and quantitative image analysis tools for kidney cancer. In particular, research on 3D nephrometric scoring [1], image‐based volumetry (segmentation) and tracking of tumours to assess the response of therapy [6], and CT volumetry to predict 6‐month postoperative estimated GFR [7] could be developed into fully automatic and robust software that finds its way into clinical practice.In conclusion, this paper [1] on 3D models for nephrometric scoring outlines another exciting new way in which advanced image analysis techniques might improve nephrometric scoring and the prediction of complications.

by Lorenz Berger and Faiz Mumtaz

References

  1. Porpiglia FAmparore DCheccucci E et al. Three‐dimensional virtual imaging of the renal tumors: a new tool to improve the accuracy of nephrometric scores. BJU Int 2019; 124: 945-54
  2. Hyde ERBerger LURamachandran N et al. Interactive virtual 3D models of renal cancer patient anatomies alter partial nephrectomy surgical planning decisions and increase surgeon confidence compared to volume‐rendered images. Int J Comput Assist Radiol Surg 201914723
  3. Shirk JDKwan LSaigal CThe use of 3‐dimensional, virtual reality models for surgical planning of robotic partial nephrectomy. Urology 201912592– 7
  4. Suinesiaputra ASanghvi MMAung N et al. Fully‐automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results. Int J Cardiovasc Imaging 201834281
  5. Menze BHJakab ABauer S et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015341993– 2024
  6. Smith ADLieber MLShah SNAssessing tumor response and detecting recurrence in metastatic renal cell carcinoma on targeted therapy: importance of size and attenuation on contrast‐enhanced CT. Am J Roentgenol 2010194157– 65
  7. Corradi RKabra ASuarez M et al. Validation of 3‐D volumetric based renal function prediction calculator for nephron sparing surgery. Int Urol Nephrol 201749615

 

 

 

 

Video: Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

Three‐dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores

Abstract

Objectives

To apply the standard PADUA and RENAL nephrometry score variables to three‐dimensional (3D) virtual models (VMs) produced from standard bi‐dimensional imaging, thereby creating three‐dimensional (3D)‐based (PADUA and RENAL) nephrometry scores/categories for the reclassification of the surgical complexity of renal masses, and to compare the new 3D nephrometry score/category with the standard 2D‐based nephrometry score/category, in order to evaluate their predictive role for postoperative complications.

Materials and Methods

All patients with localized renal tumours scheduled for minimally invasive partial nephrectomy (PN) between September 2016 and September 2018 underwent 3D and 2D nephrometry score/category assessments preoperatively. After nephrometry score/category evaluation, all the patients underwent surgery. Chi‐squared tests were used to evaluate the individual patients’ grouping on the basis of the imaging tool (3D VMs and 2D imaging) used to assess the nephrometry score/category, while Cohen’s κ coefficient was used to test the concordance between classifications. Receiver‐operating characteristic curves were produced to evaluate the sensitivity and specificity of the 3D nephrometry score/category vs the 2D nephrometry score/category in predicting the occurrence of postoperative complications. A general linear model was used to perform multivariable analyses to identify predictors of overall and major postoperative complications.

Results

A total of 101 patients were included in the study. The evaluation of PADUA and RENAL nephrometry scores via 3D VMs showed a downgrading in comparison with the same scores evaluated with 2D imaging in 48.5% and 52.4% of the cases. Similar results were obtained for nephrometry categories (29.7% and 30.7% for PADUA risk and RENAL complexity categories, respectively). The 3D nephrometry score/category demonstrated better accuracy than the 2D nephrometry score/category in predicting overall and major postoperative complications (differences in areas under the curve for each nephrometry score/category were statistically significant comparing the 3D VMs with 2D imaging assessment). Multivariable analyses confirmed 3D PADUA/RENAL nephrometry category as the only independent predictors of overall (P = 0.007; P = 0.003) and major postoperative complications (P = 0.03; P = 0.003).

Conclusions

In the present study, we showed that 3D VMs were more precise than 2D standard imaging in evaluating the surgical complexity of renal masses according to nephrometry score/category. This was attributable to a better perception of tumour depth and its relationships with intrarenal structures using the 3D VM, as confirmed by the higher accuracy of the 3D VM in predicting postoperative complications.

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Article of the Week: PADUA and R.E.N.A.L. nephrometry scores correlate with perioperative outcomes of RAPN: analysis of the Vattikuti GQI-RUS database

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 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.

PADUA and R.E.N.A.L. nephrometry scores correlate with perioperative outcomes of robot-assisted partial nephrectomy: analysis of the Vattikuti Global Quality Initiative in Robotic Urologic Surgery (GQI-RUS) database

 

Riccardo Schiavina*, Giacomo Novara,, Marco Borghesi*, Vincenzo Ficarra§Rajesh Ahlawat, Daniel A. Moon**, Francesco Porpiglia††,BenjaminJ.Challacombe‡‡Prokar Dasgupta‡‡, Eugenio Brunocilla*, Gaetano La Manna§§, Alessandro Volpe¶¶Hema Verma***, Giuseppe Martorana* and Alexandre Mottrie,†††

 

*Department of Urology, University of Bologna, Bologna,† Department of Surgery, Oncology, and Gastroenterology – Urology Clinic, University of Padua, Padua, Italy, OLV Vattikuti Robotic Surgery Institute, Aalst, Belgium, §Department of
Experimental and Clinical Medical Sciences, University of Udine, Udine, Italy, Division of Urology and Renal Transplantation, Medanta Kidney and Urology Institute, Medanta-The Medicity, Gurgaon, India, **Department of Surgery, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Vic., Australia, ††San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy, ‡‡Department of Urology, Guys and St Thomas NHS Foundation Trust and National Institute for Health Research (NIHR) Biomedical Research Centre, Kings College London, London, UK, §§Department Nephrology and Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, ¶¶University of Eastern Piedmont, Novara, Italy, ***Department of Radiology, Guys and St Thomas NHS Foundation Trust and National Institute for Health Research (NIHR) Biomedical Research Centre, Kings College London, London, UK, and †††Department of Urology, Onze-Lieve-Vrouw Hospital, Aalst, Belgium

 

Read the full article

Abstract

Objectives

To evaluate and compare the correlations between Preoperative Aspects and Dimensions Used for an Anatomical (PADUA) and R.E.N.A.L. [Radius (tumour size as maximal diameter), Exophytic/endophytic properties of the tumour, Nearness of tumour deepest portion to the collecting system or sinus, Anterior (a)/posterior (p) descriptor and the Location relative to the polar line] nephrometry scores and perioperative outcomes and postoperative complications in a multicentre, international series of patients undergoing robot-assisted partial nephrectomy (RAPN) for masses suspicious for renal cell carcinoma (RCC).

Patients and Methods

We retrospectively evaluated the clinical records of patients who underwent RAPN between 2010 and 2013 for clinical N0M0 renal tumours in four international centres that completed all the data required for the Vattikuti Global Quality Initiative in Robotic Urologic Surgery (GQI-RUS) database. All patients underwent preoperative computed tomography or magnetic resonance imaging to define the clinical stage and anatomical characteristics of the tumours. PADUA and R.E.N.A.L. scores were retrospectively assessed in each centre. Univariate and multivariate analyses were used to evaluate the correlations between age, gender, Charlson comorbidity index, clinical tumour size, PADUA and R.E.N.A.L. complexity group categories and warm ischaemia time (WIT) of >20 min, urinary calyceal system closure, and grade of postoperative complications.

aotw-mar-4-results

Results

Overall, 277 patients were evaluated. The median (interquartile range) tumour size was 33.0 (22.0–43.0) mm. The median PADUA and R.E.N.A.L. scores were eight and seven, respectively; 112 (40.4%), 86 (31.0%) and 79 (28.5%) patients were classified in the low-, intermediate- or high-complexity group according to PADUA score, while 118 (42.5%), 139 (50.1%) and 20 (7.2%) were classified in the low-, intermediate- or high-complexity group according to R.E.N.A.L. score, respectively. Both nephrometry tools significantly correlated with perioperative outcomes at univariate and multivariate analyses.

Conclusion

A precise stratification of patients before PN is recommended to consider both the potential threats and benefits of nephron-sparing surgery. In our present analysis, both PADUA and R.E.N.A.L. were significantly associated with predicting prolonged WIT and high-grade postoperative complications after RAPN.

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Editorial: Nephrometry scoring systems: valuable research tools, but can they be applied in daily clinical practice?

In this issue of BJUI Schiavina et al. [1] report on the RENAL and PADUA nephrometry scoring systems in predicting peri-operative outcomes, including warm ischaemia time and postoperative complications, in a multi-institutional cohort of patients undergoing robot-assisted partial nephrectomy. The authors showed that tumours classified as being of intermediate and high complexity on the PADUA score and high complexity on the RENAL score were associated with a nearly threefold higher risk of longer warm ischaemia times (>20 min). In addition, more complex tumours carried a higher risk of grade 3–4 postoperative complications (most commonly bleeding requiring angioembolization and urine leak requiring a ureteric stent). Notably, the two scoring systems were found to be similar predictors of these peri-operative outcomes on receiver-operating curve (ROC) analyses [1].

This represents the first large, multicentre study to evaluate the accuracy of these scoring systems in a cohort of patients who purely underwent robot-assisted surgery. A recent study by Borgmann et al. [2] found that, among the reported scoring systems, the RENAL nephrometry score correlated best with achieving negative surgical margins, shorter ischaemia times, and low postoperative complication rates; however, only 9% of patients underwent robot-assisted surgery. Another contemporary series showed concordance between the RENAL and PADUA scoring systems in predicting ischaemia times and complication rates, albeit in patients who only underwent open surgery [3].

Current guidelines recognize nephron-sparing approaches to small renal masses as the standard of care in well-selected patients, with the robot-assisted platform being predominantly adopted in clinical practice where available. Certainly, these nephrometry scores are valuable for urologists in counselling patients on the potential risk of complications specific to the surgical anatomy of the tumour. In addition, the RENAL and PADUA scores (and others) provide a quantitative, objective method for comparing data from different studies and different institutions.

As nephrometry scoring systems continue to be critically evaluated in the robotic surgery era, the question that naturally arises is: which system is best? With regard to this question, the data in the present study do not necessarily favour one or the other for the prediction of clinically relevant peri-operative outcomes. One must recognize, however, that several other anatomy-based scoring systems exist and were not examined in this manuscript [4-6]. While these are very valuable research and patient counselling tools, one must caution against using these nephrometry tools to make clinical decisions; for example, attempting to predict benign vs malignant histology (without a biopsy), attempting to predict high vs low grade tumours, or deciding on whether to perform a radical vs partial nephrectomy, or an open vs minimally invasive approach. After all, one must keep in mind that the area under the curve for these tools is in the range of 0.58–0.63 (0.50 being equivalent to toss of a coin).

It would have been interesting to include clinical size only in the present multivariate analysis (as was done for RENAL and PADUA scoring) and ROC analysis to compare this simple variable with the studied nephrometry scores. Future research should examine additional confounders that could potentially affect postoperative complication rates, such as BMI, adherent perinephric fat, experience of the surgeon actually performing the partial nephrectomy, technique of resection used (e.g. enucleation or resection) among others. This may help to distinguish a single system as the optimum model for use in research and in patient counselling regarding potential postoperative complications.

Matthew A. Meissner and Jose A. Karam

 

Department of Urology, University of Texas MD Andersonn Cancer Center, Houston, TX, USA

 

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References

 

 

 

3 Kriegmair MC, Mandel P, Moses A et al. Dening Renal Masses: comprehensive Comparison of RENAL, PADUA, NePhRO, and C-Index Score. Clin Genitourin Cancer 2016; [Epub ahead of print]. doi: 10.1016/ j.clgc.2016.07.029.

 

 

5 Hakky TS, Baumgarten AS, Allen B, Lin HY, Ercole CE, Sexton WJSpiess PE et al. Zonal NePhRO scoring system: a superior renal tumor complexity classication model. Clin Genitourin Cancer 2014; 12: e138

 

6 Simmons MN, Ching CB, Samplaski MK, Park CH, Gill IS et al. Kidney tumor location measurement using the C index method. J Urol 2010; 183: 170813

 

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