Tag Archive for: aotw 04-12-19

Posts

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.

View more videos
© 2022 BJU International. All Rights Reserved.