Advertisement

Development and Validity of a Silicone Renal Tumor Model for Robotic Partial Nephrectomy Training

Published:February 05, 2018DOI:https://doi.org/10.1016/j.urology.2018.01.030

      Objective

      To provide a training tool to address the technical challenges of robot-assisted laparoscopic partial nephrectomy, we created silicone renal tumor models using 3-dimensional printed molds of a patient's kidney with a mass. In this study, we assessed the face, content, and construct validity of these models.

      Materials and Methods

      Surgeons of different training levels completed 4 simulations on silicone renal tumor models. Participants were surveyed on the usefulness and realism of the model as a training tool. Performance was measured using operation-specific metrics, self-reported operative demands (NASA Task Load Index [NASA TLX]), and blinded expert assessment (Global Evaluative Assessment of Robotic Surgeons [GEARS]).

      Results

      Twenty-four participants included attending urologists, endourology fellows, urology residents, and medical students. Post-training surveys of expert participants yielded mean results of 79.2 on the realism of the model's overall feel and 90.2 on the model's overall usefulness for training. Renal artery clamp times and GEARS scores were significantly better in surgeons further in training (P ≤.005 and P ≤.025). Renal artery clamp times, preserved renal parenchyma, positive margins, NASA TLX, and GEARS scores were all found to improve across trials (P <.001, P = .025, P = .024, P ≤.020, and P ≤.006, respectively).

      Conclusion

      Face, content, and construct validity were demonstrated in the use of a silicone renal tumor model in a cohort of surgeons of different training levels. Expert participants deemed the model useful and realistic. Surgeons of higher training levels performed better than less experienced surgeons in various study metrics, and improvements within individuals were observed over sequential trials. Future studies should aim to assess model predictive validity, namely, the association between model performance improvements and improvements in live surgery.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Urology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • National Cancer Institute
        SEER Cancer Stat Facts: Kidney and Renal Pelvis Cancer.
        (Bethesda, MD; Available at)
        • Gandaglia G.
        • Ravi P.
        • Abdollah F.
        • et al.
        Contemporary incidence and mortality rates of kidney cancer in the United States.
        Can Urol Assoc J. 2014; 8: 247-252https://doi.org/10.5489/cuaj.1760
        • Liu J.-J.
        • Leppert J.T.
        • Maxwell B.G.
        • Panousis P.
        • Chung B.I.
        Trends and perioperative outcomes for laparoscopic and robotic nephrectomy using the National Surgical Quality Improvement Program (NSQIP).
        Urol Oncol Semin Orig Investig. 2014; 32: 473-479https://doi.org/10.1016/j.urolonc.2013.09.012
        • Sammon J.
        • Petros F.
        • Sukumar S.
        • et al.
        Barbed suture for renorrhaphy during robot-assisted partial nephrectomy.
        J Endourol. 2011; 25: 529-533https://doi.org/10.1089/end.2010.0455
        • Aboumarzouk O.M.
        • Stein R.J.
        • Eyraud R.
        • et al.
        Robotic versus laparoscopic partial nephrectomy: a systematic review and meta-analysis.
        Eur Urol. 2012; 62: 1023-1033https://doi.org/10.1016/j.eururo.2012.06.038
        • Funahashi Y.
        • Yoshino Y.
        • Sassa N.
        • Matsukawa Y.
        • Takai S.
        • Gotoh M.
        Comparison of warm and cold ischemia on renal function after partial nephrectomy.
        Urology. 2014; 84: 1408-1413https://doi.org/10.1016/j.urology.2014.08.040
        • Mottrie A.
        • De Naeyer G.
        • Schatteman P.
        • Carpentier P.
        • Sangalli M.
        • Ficarra V.
        Impact of the learning curve on perioperative outcomes in patients who underwent robotic partial nephrectomy for parenchymal renal tumours.
        Eur Urol. 2010; 58: 127-133https://doi.org/10.1016/j.eururo.2010.03.045
        • Volpe A.
        • Blute M.L.
        • Ficarra V.
        • et al.
        Renal ischemia and function after partial nephrectomy: a collaborative review of the literature.
        Eur Urol. 2015; 68: 61-74https://doi.org/10.1016/j.eururo.2015.01.025
        • Hung A.J.
        • Ng C.K.
        • Patil M.B.
        • et al.
        Validation of a novel robotic-assisted partial nephrectomy surgical training model.
        BJU Int. 2012; 110: 870-874https://doi.org/10.1111/j.1464-410X.2012.10953.x
        • Taylor G.D.
        • Johnson D.B.
        • Hogg D.C.
        • Cadeddu J.A.
        Development of a renal tumor mimic model for learning minimally invasive nephron sparing surgical techniques.
        J Urol. 2004; 172: 382-385https://doi.org/10.1097/01.ju.0000132358.82641.10
        • Wake N.
        • Rude T.
        • Kang S.K.
        • et al.
        3D printed renal cancer models derived from MRI data: application in pre-surgical planning.
        Abdom Radiol. 2017; 42: 1501-1509https://doi.org/10.1007/s00261-016-1022-2
        • von Rundstedt F.-C.
        • Scovell J.M.
        • Agrawal S.
        • Zaneveld J.
        • Link R.E.
        Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy.
        BJU Int. 2017; 119: 598-604https://doi.org/10.1111/bju.13712
        • Cheung C.L.
        • Looi T.
        • Lendvay T.S.
        • Drake J.M.
        • Farhat W.A.
        Use of 3-dimensional printing technology and silicone modeling in surgical simulation: development and face validation in pediatric laparoscopic pyeloplasty.
        J Surg Educ. 2014; 71: 762-767https://doi.org/10.1016/j.jsurg.2014.03.001
        • Schout B.M.A.
        • Hendrikx A.J.M.
        • Scherpbier A.J.J.A.
        • Bemelmans B.L.H.
        Update on training models in endourology: a qualitative systematic review of the literature between January 1980 and April 2008.
        Eur Urol. 2008; 54: 1247-1261https://doi.org/10.1016/j.eururo.2008.06.036
        • McDougall E.M.
        Validation of surgical simulators.
        J Endourol. 2007; 21https://doi.org/10.1089/end.2007.9985
        • Kutikov A.
        • Uzzo R.G.
        The R.E.N.A.L. nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth.
        J Urol. 2009; 182: 844-853https://doi.org/10.1016/j.juro.2009.05.035
        • Mir M.C.
        • Ercole C.
        • Takagi T.
        • et al.
        Decline in renal function after partial nephrectomy: etiology and prevention.
        J Urol. 2015; 193: 1889-1898https://doi.org/10.1016/j.juro.2015.01.093
        • Hart S.G.
        • Staveland L.E.
        Development of NASA-TLX (Task Load Index): results of empirical and theoretical research.
        Adv Psychol. 1988; 52: 139-183https://doi.org/10.1016/S0166-4115(08)62386-9
        • Zheng B.
        • Jiang X.
        • Tien G.
        • Meneghetti A.
        • Panton O.N.M.
        • Atkins M.S.
        Workload assessment of surgeons: correlation between NASA TLX and blinks.
        Surg Endosc. 2012; 26: 2746-2750https://doi.org/10.1007/s00464-012-2268-6
        • Aghazadeh M.A.
        • Jayaratna I.S.
        • Hung A.J.
        • et al.
        External validation of Global Evaluative Assessment of Robotic Skills (GEARS).
        Surg Endosc. 2015; 29: 3261-3266https://doi.org/10.1007/s00464-015-4070-8
        • Alamri A.A.
        • Abdulla A.
        • Madjeruh J.
        • et al.
        Designing a high-fidelity laparoscopic partial nephrectomy bench model: determining the tear strength and resistance of a synthetic silicone composition.
        J Urol. 2011; 185: e597-e598https://doi.org/10.1016/j.juro.2011.02.1449
        • Alamri A.
        • Abdulla A.
        • Madjeruh J.
        • Matsumoto E.D.
        Validation of a partial nephrectomy bench model developed via a novel material engineering process.
        J Urol. 2012; 187: e608https://doi.org/10.1016/j.juro.2012.02.1270
        • Chang D.R.
        • Lin R.P.
        • Bowe S.
        • et al.
        Fabrication and validation of a low-cost, medium-fidelity silicone injection molded endoscopic sinus surgery simulation model.
        Laryngoscope. 2017; 127: 781-786https://doi.org/10.1002/lary.26370
        • Ryan J.R.
        • Almefty K.K.
        • Nakaji P.
        • Frakes D.H.
        Cerebral aneurysm clipping surgery simulation using patient-specific 3d printing and silicone casting.
        World Neurosurg. 2016; 88: 175-181https://doi.org/10.1016/j.wneu.2015.12.102
        • Grober E.D.
        • Hamstra S.J.
        • Wanzel K.R.
        • et al.
        The educational impact of bench model fidelity on the acquisition of technical skill: the use of clinically relevant outcome measures.
        Ann Surg. 2004; 240: 374-381https://doi.org/10.1097/01.sla.0000133346.07434.30
        • Matsumoto E.D.
        • Hamstra S.J.
        • Radomski S.B.
        • Cusimano M.D.
        The effect of bench model fidelity on endourological skills: a randomized controlled study.
        J Urol. 2002; 167: 1243-1247https://doi.org/10.1016/S0022-5347(05)65274-3