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The role of exposure time in computerized training of prostate cryosurgery: performance comparison of surgical residents with engineering students

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Abstract

Purpose

This study aims at the evaluation of a prototype of a computerized trainer for cryosurgery—the controlled destruction of cancer tumors by freezing. The hypothesis in this study is that computer-based cryosurgery training for an optimal cryoprobe layout is essentially a matter of exposure time, rather than trainee background or the specific computer-generated planning target. Key geometric features under considerations are associated with spatial limitations on cryoprobes placement and the match between the resulted thermal field and the unique anatomy of the prostate.

Methods

All experiments in this study were performed on the cryosurgery trainer—a prototype platform for computerized cryosurgery training, which has been presented previously. Among its key features, the cryosurgery trainer displays the prostate shape and its contours and provides a distance measurement tool on demand, in order to address spatial constraints during ultrasound imaging guidance. Another unique feature of the cryosurgery trainer is an output movie, displaying the simulated thermal field at the end of the cryoprocedure.

Results

The current study was performed on graduate engineering students having no formal background in medicine, and the results were benchmarked against data obtained on surgical residents having no experience with cryosurgery. Despite fundamental differences in background and experience, neither group displayed superior performance when it comes to cryoprobe layout planning.

Conclusions

This study demonstrates that computer-based training of an optimal cryoprobe layout is feasible. This study demonstrates that the training quality is essentially related to the training exposure time, rather than to a specific planning strategy from those investigated.

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References

  1. Gage AA, Baust J (1998) Mechanisms of tissue injury in cryosurgery. Cryobiology 37(3):171–186. https://doi.org/10.1006/cryo.1998.2115

    Article  CAS  PubMed  Google Scholar 

  2. Babaian RJ, Donnelly B, Bahn D, Baust JG, Dineen M, Ellis D, Katz A, Pisters L, Rukstalis D, Shinohara K, Thrasher JB (2008) Best practice statement on cryosurgery for the treatment of localized prostate cancer. J Urol 180(5):1993–2004. https://doi.org/10.1016/j.juro.2008.07.108

    Article  PubMed  Google Scholar 

  3. Rabin Y, Lung DC, Stahovich TF (2004) Computerized planning of cryosurgery using cryoprobes and cryoheaters. Technol Cancer Res Treat 3(3):229–243. https://doi.org/10.1177/153303460400300301

    Article  PubMed  Google Scholar 

  4. Sehrawat A, Keelan R, Shimada K, Wilfong DM, Mccormick JT, Rabin Y (2016) Simulation-based cryosurgery intelligent tutoring system prototype. Technol Cancer Res Treat 15(2):396–407. https://doi.org/10.1177/1533034615583187

    Article  PubMed  Google Scholar 

  5. Baissalov R, Sandison GA, Donnelly BJ, Saliken JC, McKinnon JG, Muldrew K, Rewcastle JC (2000) A semi-empirical treatment planning model for optimization of multiprobe cryosurgery. Phys Med Biol 45(5):1085–98. https://doi.org/10.1088/0031-9155/45/5/301

    Article  CAS  PubMed  Google Scholar 

  6. Baissalov R, Sandison GA, Reynolds D, Muldrew K (2001) Simultaneous optimization of cryoprobe placement and thermal protocol for cryosurgery. Phys Med Biol 46(7):1799–814. https://doi.org/10.1088/0031-9155/46/7/305

    Article  CAS  PubMed  Google Scholar 

  7. Keanini RG, Rubinsky B (1992) Optimization of multiprobe cryosurgery. ASME J Heat Trans 114(4):796–801. https://doi.org/10.1115/1.2911885

    Article  Google Scholar 

  8. Keelan R, Yamakawa S, Shimada K, Rabin Y (2013) Computerized training of cryosurgery—a system approach. CryoLetters 34(4):324–337

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Lung DC, Stahovich TF, Rabin Y (2004) Computerized planning for multiprobe cryosurgery using a force-field analogy. Comput Methods Biomech Biomed Eng 7(2):101–110. https://doi.org/10.1080/10255840410001689376

    Article  Google Scholar 

  10. Rossi MR, Tanaka D, Shimada K, Rabin Y (2010) Computerized planning of prostate cryosurgery using variable cryoprobe insertion depth. Cryobiology 60(1):71–79. https://doi.org/10.1016/j.cryobiol.2008.11.008

    Article  PubMed  Google Scholar 

  11. Rossi MR, Tanaka D, Shimada K, Rabin Y (2007) An efficient numerical technique for bioheat simulations and its application to computerized cryosurgery planning. Comput Methods Programs Biomed 85(1):41–50. https://doi.org/10.1016/j.cmpb.2006.09.014

    Article  PubMed  Google Scholar 

  12. Tanaka D, Shimada K, Rabin Y (2006) Two-phase computerized planning of cryosurgery using bubble-packing and force-field analogy. J Biomech Eng 128(1):49–58. https://doi.org/10.1115/1.2136166

    PubMed  PubMed Central  Google Scholar 

  13. Cresswell J, Asterling S, Chaudhary M, Sheikh N, Greene D (2006) Third-generation cryotherapy for prostate cancer in the UK: a prospective study of the early outcomes in primary and recurrent disease. BJU Int 97(5):969–74. https://doi.org/10.1111/j.1464-410X.2006.06073.x

    Article  PubMed  Google Scholar 

  14. Wong WS, Chinn DO, Chinn M, Chinn J, Tom WL (1997) Cryosurgery as a treatment for prostate carcinoma: results and complications. Cancer 79(5):963–74. https://doi.org/10.1002/(SICI)1097-0142(19970301)79:5%3c963::AID-CNCR13%3e3.0.CO;2-0

    Article  CAS  PubMed  Google Scholar 

  15. Ziv A, Small SD, Wolpe PR (2000) Patient safety and simulation-based medical education. Med Teach 22:489–495. https://doi.org/10.1080/01421590050110777

    Article  Google Scholar 

  16. Keelan R, Zhang H, Shimada K, Rabin Y (2016) Graphics processing unit-based bioheat simulation to facilitate rapid decision making associated with cryosurgery training. Technol Cancer Res Treat 15(2):377–86. https://doi.org/10.1177/1533034615580694

    Article  PubMed  Google Scholar 

  17. Keelan R, Shimada K, Rabin Y (2017) GPU-based simulation of ultrasound imaging artifacts for cryosurgery training. Technol Cancer Res Treat 16(1):5–14. https://doi.org/10.1177/1533034615623062

    Article  PubMed  Google Scholar 

  18. Rabin Y, Shimada K, Joshi P, Sehrawat A, Keelan R, Wilfong DM, McCormick JT (2017) A computerized tutor prototype for prostate cryotherapy: key building blocks and system evaluation. In: Proceesings of SPIE 10066, energy-based treatment of tissue and assessment, vol IX, pp 100660Z-10066-9. https://doi.org/10.1117/12.2257151

  19. Sehrawat A, Shimada K, Rabin Y (2013) Generating prostate models by means of geometric deformation with application to computerized training of cryosurgery. Int J Comput Assist Radiol Surg 8(2):301–312. https://doi.org/10.1007/s11548-012-0780-8

    Article  PubMed  Google Scholar 

  20. Sehrawat A, Keelan R, Shimada K, Wilfong DM, Mccormick JT, Rabin Y (2016) Simulation-based cryosurgery training: variable insertion depth planning in prostate cryosurgery. Technol Cancer Res Treat 15(2):396–407. https://doi.org/10.1177/1533034615583187

    Article  PubMed  Google Scholar 

  21. Pennes HH (1948) Analysis of tissue and arterial blood temperatures in the resting human forearm. J Appl Phys 1(2):93–122

    CAS  Google Scholar 

  22. Joshi P, Sehrawat A, Rabin Y (2017) Computerized planning of prostate cryosurgery and shape considerations. Technol Cancer Res Treat. https://doi.org/10.1177/1533034617716041

    PubMed  PubMed Central  Google Scholar 

  23. Furuhata T, Song I, Rabin Y, Shimada K (2014) Interactive prostate shape reconstruction from 3D TRUS images. J Comput Des Eng 1(4):272–288. https://doi.org/10.7315/JCDE.2014.027

    PubMed  PubMed Central  Google Scholar 

  24. Fisher RA (1934) Statistical methods for research workers. Edinburgh, London

    Google Scholar 

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Funding

This study has been supported in part by Grant Number R01CA134261 to from the National Cancer Institute (USA) to Yoed Rabin. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Purva Joshi, Anjali Sehrawat & Yoed Rabin

Authors
  1. Purva Joshi
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  2. Anjali Sehrawat
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  3. Yoed Rabin
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Corresponding author

Correspondence to Yoed Rabin.

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Conflict of interest

The authors declare no conflict of interest. Yoed Rabin is a board member of the American College of Cryosurgery and a member of the Board of Governors of the International Society for Cryosurgery.

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Joshi, P., Sehrawat, A. & Rabin, Y. The role of exposure time in computerized training of prostate cryosurgery: performance comparison of surgical residents with engineering students. Int J CARS 13, 541–549 (2018). https://doi.org/10.1007/s11548-017-1700-8

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  • DOI: https://doi.org/10.1007/s11548-017-1700-8

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