OBJECTIVE: To evaluate the effect of 3-dimensionally (3D) printed physical renal models with enhancing masses on medical trainee characterization, localization, and understanding of renal malignancy. METHODS: Proprietary software was used to import standard computed tomography (CT) cross-sectional imaging into 3D printers to create physical models of renal units with enhancing renal lesions in situ. Six different models were printed from a transparent plastic resin; the normal parenchyma was printed in a clear, translucent plastic, with a red hue delineating the suspicious renal lesion. Medical students, who had completed their first year of training, were given an overview and tasked with completion of RENAL nephrometry scores, separately using CT imaging and 3D models. Trainees were also asked to complete a questionnaire about their experience. Variability between trainees was assessed by intraclass correlation coefficients (ICCs), and kappa statistics were used to compare the trainee to experts. RESULTS: Overall trainee nephrometry score accuracy was significantly improved with the 3D model vs CT scan (P <.01). Furthermore, 3 of the 4 components of the nephrometry score (radius, nearness to collecting system, and location) showed significant improvement (P <.001) using the models. There was also more consistent agreement among trainees when using the 3D models compared with CT scans to assess the nephrometry score (intraclass correlation coefficient, 0.28 for CT scan vs 0.72 for 3D models). Qualitative evaluation with questionnaires filled out by the trainees further confirmed that the 3D models improved their ability to understand and conceptualize the renal mass. CONCLUSION: Physical 3D models using readily available printing techniques improve trainees' understanding and characterization of individual patients' enhancing renal lesions. Published by Elsevier Inc.
OBJECTIVE: To evaluate the effect of 3-dimensionally (3D) printed physical renal models with enhancing masses on medical trainee characterization, localization, and understanding of renal malignancy. METHODS: Proprietary software was used to import standard computed tomography (CT) cross-sectional imaging into 3D printers to create physical models of renal units with enhancing renal lesions in situ. Six different models were printed from a transparent plastic resin; the normal parenchyma was printed in a clear, translucent plastic, with a red hue delineating the suspicious renal lesion. Medical students, who had completed their first year of training, were given an overview and tasked with completion of RENAL nephrometry scores, separately using CT imaging and 3D models. Trainees were also asked to complete a questionnaire about their experience. Variability between trainees was assessed by intraclass correlation coefficients (ICCs), and kappa statistics were used to compare the trainee to experts. RESULTS: Overall trainee nephrometry score accuracy was significantly improved with the 3D model vs CT scan (P <.01). Furthermore, 3 of the 4 components of the nephrometry score (radius, nearness to collecting system, and location) showed significant improvement (P <.001) using the models. There was also more consistent agreement among trainees when using the 3D models compared with CT scans to assess the nephrometry score (intraclass correlation coefficient, 0.28 for CT scan vs 0.72 for 3D models). Qualitative evaluation with questionnaires filled out by the trainees further confirmed that the 3D models improved their ability to understand and conceptualize the renal mass. CONCLUSION: Physical 3D models using readily available printing techniques improve trainees' understanding and characterization of individual patients' enhancing renal lesions. Published by Elsevier Inc.
Authors: Todd G Manning; Jonathan S O'Brien; Daniel Christidis; Marlon Perera; Jasamine Coles-Black; Jason Chuen; Damien M Bolton; Nathan Lawrentschuk Journal: World J Urol Date: 2018-01-25 Impact factor: 4.226
Authors: Friedrich-Carl von Rundstedt; Jason M Scovell; Smriti Agrawal; Jacques Zaneveld; Richard E Link Journal: BJU Int Date: 2016-12-10 Impact factor: 5.588
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