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Rapid Segmentation of Renal Tumours to Calculate Volume Using 3D Interpolation

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Abstract

Small renal masses are commonly diagnosed with modern medical imaging. Renal tumour volume has been explored as a prognostic tool to help decide when intervention is needed and appears to provide additional prognostic information for smaller tumours compared with tumour diameter. However, the current method of calculating tumour volume in clinical practice uses the ellipsoid equation (π/6 × length × width × height) which is an oversimplified approach. Some research groups trace the contour of the tumour in every image slice which is impractical for clinical use. In this study, we demonstrate a method of using 3D segmentation software and the 3D interpolation method to rapidly calculate renal tumour volume in under a minute. Using this method in 27 patients that underwent radical or partial nephrectomy, we found a 10.07% mean absolute difference compared with the traditional ellipsoid method. Our segmentation volume was closer to the calculated histopathological tumour volume than the traditional method (p = 0.03) with higher Lin’s concordance correlation coefficient (0.79 vs 0.72). 3D segmentation has many uses related to 3D printing and modelling and is becoming increasingly common. Calculation of tumour volume is one additional benefit it provides. Further studies on the association between segmented tumour volume and prognosis are needed.

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References

  1. O'Connor SD, Pickhardt PJ, Kim DH, Oliva MR, Silverman SG. Incidental finding of renal masses at unenhanced CT: Prevalence and analysis of features for guiding management. AJR American journal of roentgenology. 197(1):139-45, 2011

    Article  Google Scholar 

  2. Ali S, Ahn T, Papa N, Perera M, Teloken P, Coughlin G, et al; Changing trends in surgical management of renal tumours from 2000 to 2016: a nationwide study of Medicare claims data. ANZ J Surg, 2019

  3. Wah TM, Irving HC, Gregory W, Cartledge J, Joyce AD, Selby PJ. Radiofrequency ablation (RFA) of renal cell carcinoma (RCC): Experience in 200 tumours. BJU Int. 113(3):416-28, 2014

    Article  Google Scholar 

  4. Warren AY, Harrison D. WHO/ISUP classification, grading and pathological staging of renal cell carcinoma: Standards and controversies. World J Urol. 36(12):1913-26, 2018

    Article  Google Scholar 

  5. Jorns J, Thiel DD, Lohse CM, Williams A, Arnold ML, Cheville JC, et al; Three-dimensional tumour volume and cancer-specific survival for patients undergoing nephrectomy to treat pT1 clear-cell renal cell carcinoma. BJU Int. 110(7):956-60, 2012

    Article  Google Scholar 

  6. Choi SM, Choi DK, Kim TH, Jeong BC, Seo SI, Jeon SS, et al; A comparison of radiologic tumor volume and pathologic tumor volume in renal cell carcinoma (RCC). PloS one. 10(3):e0122019-e, 2015

  7. Chen MY, Skewes J, Desselle M, Wong C, Woodruff MA, Dasgupta P, et al; Current applications of three-dimensional printing in urology. BJU Int. 125(1):17-27, 2020

    Article  Google Scholar 

  8. Chen MY, Skewes J, Woodruff MA, Rukin NJ. Using bespoke 3D-printed models to improve patient understanding of an encrusted ureteric stent. J Clin Urol. 2051415819876514, 2019

  9. Checcucci E, de Cillis S, Porpiglia F, (2020) 3D-printed models and virtual reality as new tools for image-guided robot-assisted nephronsparing surgery. Curr Opin Urol 30 (1):55-64

    Article  Google Scholar 

  10. Hyde ER, Berger LU, Ramachandran N, Hughes-Hallett A, Pavithran NP, Tran MGB, 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. 14(4):723-32, 2019

    Article  CAS  Google Scholar 

  11. Kamai T, Furuya N, Kambara T, Abe H, Honda M, Shioyama Y, et al; Single minimum incision endoscopic radical nephrectomy for renal tumors with preoperative virtual navigation using 3D-CT volume-rendering. BMC Urol. 10:7, 2019

    Article  Google Scholar 

  12. Porpiglia F, Amparore D, Checcucci E, Manfredi M, Stura I, Migliaretti G, et al; Three-dimensional virtual imaging of renal tumours: a new tool to improve the accuracy of nephrometry scores. BJU Int. 124(6):945-54, 2019

    Article  Google Scholar 

  13. Shirk JD, Thiel DD, Wallen EM, Linehan JM, White WM, Badani KK, et al; Effect of 3-dimensional virtual reality models for surgical planning of robotic-assisted partial nephrectomy on surgical outcomes: a randomized clinical trial. JAMA Netw Open. 2(9):e1911598-e, 2019

  14. Thiel DD, Jorns J, Lohse CM, Cheville JC, Thompson RH, Parker AS. Maximum tumor diameter is not an accurate predictor of renal cell carcinoma tumor volume. Scand J Urol. 47(6):472-5, 2013

    Article  Google Scholar 

  15. Smaldone MC, Kutikov A, Egleston BL, Canter DJ, Viterbo R, Chen DYT, et al; Small renal masses progressing to metastases under active surveillance: a systematic review and pooled analysis. Cancer. 118(4):997-1006, 2012

    Article  Google Scholar 

  16. Secil M, Cullu N, Aslan G, Mungan U, Uysal F, Tuna B, et al; The effect of tumor volume on survival in patients with renal cell carcinoma. Diagn Interv Radiol (Ankara, Turkey). 18(5):480-7, 2012

    Google Scholar 

  17. Breau RH, Clark E, Bruner B, Cervini P, Atwell T, Knoll G, et al; A simple method to estimate renal volume from computed tomography. Can Urol Assoc J. 7(5-6):189-92, 2013

    Article  Google Scholar 

  18. Zhang J, Kang SK, Wang L, Touijer A, Hricak H; Distribution of Renal Tumor Growth Rates Determined by Using Serial Volumetric CT Measurements. Radiology. 250(1):137-44, 2009

    Article  Google Scholar 

  19. Herr HW, Lee CT, Sharma S, Hilton S. Radiographic versus pathologic size of renal tumors: Implications for partial nephrectomy. Urology. 58(2):157-60, 2001

    Article  CAS  Google Scholar 

  20. Kurta JM, Thompson RH, Kundu S, Kaag M, Manion MT, Herr HW, et al; Contemporary imaging of patients with a renal mass: does size on computed tomography equal pathological size? BJU Int. 103(1):24-7, 2009

    Article  Google Scholar 

  21. Zhang N, Wu Y, Wang J, Xu J, Na R, Wang X; The effect of discrepancy between radiologic size and pathologic tumor size in renal cell cancer. SpringerPlus. 5(1):899, 2016

    Article  Google Scholar 

  22. Khan I, Beksac AT, Paulucci DJ, Abaza R, Eun DD, Bhandari A, et al; Differences in Renal Tumor Size Measurements for Computed Tomography Versus Magnetic Resonance Imaging: Implications for Patients on Active Surveillance. J Laparoendosc Adv Surg Tech. 27(12):1275-8, 2017

    Article  Google Scholar 

  23. Guo Z, Guo N, Gong K, Zhong S, Li Q; Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network. Phys Med Biol. 64(20):205015, 2019

    Article  Google Scholar 

  24. Yousefi S, Sokooti H, Elmahdy MS, Peters FP, Shalmani MTM, Zinkstok RT, et al; editors. Esophageal Gross Tumor Volume Segmentation Using a 3D Convolutional Neural Network. Medical Image Computing and Computer Assisted Intervention – MICCAI.  2018 2018//; Cham: Springer International Publishing, 2018

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Authors and Affiliations

  1. Department of Urology, Redcliffe Hospital, Redcliffe, QLD, Australia

    Michael Y. Chen & Nicholas J. Rukin

  2. School of Medicine, University of Queensland, Brisbane, Australia

    Michael Y. Chen

  3. Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia

    Michael Y. Chen, Maria A. Woodruff & Nicholas J. Rukin

  4. Wesley Hospital, Brisbane, QLD, Australia

    Boon Kua

Authors
  1. Michael Y. Chen
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  2. Maria A. Woodruff
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  3. Boon Kua
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  4. Nicholas J. Rukin
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Correspondence to Michael Y. Chen.

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The authors declare that they have no conflict of interest.

Ethical Approval and Informed Consent

This research study was conducted retrospectively from data obtained for clinical purposes. We consulted extensively with the human research ethics committee of Royal Brisbane and Women’s Hospital who determined that our study did not need ethical approval. An official waiver of ethical approval was granted from the Royal Brisbane and Women’s Hospital human research ethics committee (HREC) study reference LNR/2019/QRBW/51,927. Written consent was not required for this retrospective study with no identifying information.

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Chen, M.Y., Woodruff, M.A., Kua, B. et al. Rapid Segmentation of Renal Tumours to Calculate Volume Using 3D Interpolation. J Digit Imaging 34, 351–356 (2021). https://doi.org/10.1007/s10278-020-00416-z

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  • DOI: https://doi.org/10.1007/s10278-020-00416-z

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