OBJECTIVE: To construct high-fidelity, patient customized, physical, 3-dimensional (3D) models of renal units with enhancing renal lesions identified on cross-sectional imaging, which may aid patients, trainees, and clinicians in their comprehension, characterization, localization, and extirpation of suspicious renal masses. METHODS: Specialized software was used to import patient's diagnostic computerized tomography cross-sectional imaging into 3D printers and create physical 3D models of renal units with enhancing in situ lesions. Patients and trainees had the opportunity to manipulate the individualized model before surgical resection. Sterolithography additive manufacturing, a technique in which an ultraviolet laser is used to cure a photosensitive resin in sequential horizontally oriented layers, was used to build the models (Medical Modeling Inc., Golden, CO). Normal renal parenchyma was printed with a clear translucent resin, and red translucent resin delineated suspicious lesions. Renal vasculature and the proximal collecting system were printed in some models. RESULTS: We constructed 5 physical models of renal units with suspected malignancies before surgery. All patients successfully underwent partial nephrectomy (4 robotic and 1 open). Average ischemia time was 21 minutes, nephrometry score was 6.8, and all margins were negative. Anecdotally, patients, their families, and trainees consistently stated that the models enhanced their comprehension of the renal tumor in relation to surrounding normal renal parenchyma and hilar structures and improved understanding of the goals of the surgery. CONCLUSION: Preoperative physical 3D models using available printing techniques can be constructed and may potentially influence both patients' and trainees' understanding of renal malignancies.
OBJECTIVE: To construct high-fidelity, patient customized, physical, 3-dimensional (3D) models of renal units with enhancing renal lesions identified on cross-sectional imaging, which may aid patients, trainees, and clinicians in their comprehension, characterization, localization, and extirpation of suspicious renal masses. METHODS: Specialized software was used to import patient's diagnostic computerized tomography cross-sectional imaging into 3D printers and create physical 3D models of renal units with enhancing in situ lesions. Patients and trainees had the opportunity to manipulate the individualized model before surgical resection. Sterolithography additive manufacturing, a technique in which an ultraviolet laser is used to cure a photosensitive resin in sequential horizontally oriented layers, was used to build the models (Medical Modeling Inc., Golden, CO). Normal renal parenchyma was printed with a clear translucent resin, and red translucent resin delineated suspicious lesions. Renal vasculature and the proximal collecting system were printed in some models. RESULTS: We constructed 5 physical models of renal units with suspected malignancies before surgery. All patients successfully underwent partial nephrectomy (4 robotic and 1 open). Average ischemia time was 21 minutes, nephrometry score was 6.8, and all margins were negative. Anecdotally, patients, their families, and trainees consistently stated that the models enhanced their comprehension of the renal tumor in relation to surrounding normal renal parenchyma and hilar structures and improved understanding of the goals of the surgery. CONCLUSION: Preoperative physical 3D models using available printing techniques can be constructed and may potentially influence both patients' and trainees' understanding of renal malignancies.
Authors: Dimitris Mitsouras; Peter Liacouras; Amir Imanzadeh; Andreas A Giannopoulos; Tianrun Cai; Kanako K Kumamaru; Elizabeth George; Nicole Wake; Edward J Caterson; Bohdan Pomahac; Vincent B Ho; Gerald T Grant; Frank J Rybicki Journal: Radiographics Date: 2015 Nov-Dec Impact factor: 5.333
Authors: John C Nouls; Rohan S Virgincar; Alexander G Culbert; Nathann Morand; Dana W Bobbert; Anne D Yoder; Robert S Schopler; Mustafa R Bashir; Alexandra Badea; Ute Hochgeschwender; Bastiaan Driehuys Journal: J Med Imaging (Bellingham) Date: 2019-05-15
Authors: Jean V Joseph; Ralph Brasacchio; Chunkit Fung; Jay Reeder; Kevin Bylund; Deepak Sahasrabudhe; Shu Yuan Yeh; Ahmed Ghazi; Patrick Fultz; Deborah Rubens; Guan Wu; Eric Singer; Edward Schwarz; Supriya Mohile; James Mohler; Dan Theodorescu; Yi Fen Lee; Paul Okunieff; David McConkey; Hani Rashid; Chawnshang Chang; Yves Fradet; Khurshid Guru; Janet Kukreja; Gerald Sufrin; Yair Lotan; Howard Bailey; Katia Noyes; Seymour Schwartz; Kathy Rideout; Gennady Bratslavsky; Steven C Campbell; Ithaar Derweesh; Per-Anders Abrahamsson; Mark Soloway; Leonard Gomella; Dragan Golijanin; Robert Svatek; Thomas Frye; Seth Lerner; Ganesh Palapattu; George Wilding; Michael Droller; Donald Trump Journal: Bladder Cancer Date: 2018-10-03
Authors: Nicole Wake; Temitope Rude; Stella K Kang; Michael D Stifelman; James F Borin; Daniel K Sodickson; William C Huang; Hersh Chandarana Journal: Abdom Radiol (NY) Date: 2017-05