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Nikulin A, Diduev G, Romanikhin A, Khorobrykh T, Ivashov I, Singh TA. Video-assisted thoracoscopic surgery for prevention of postpneumonectomy syndrome: A case report. Int J Surg Case Rep 2023; 111:108738. [PMID: 37778135 PMCID: PMC10562148 DOI: 10.1016/j.ijscr.2023.108738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023] Open
Abstract
INTRODUCTION Postpneumonectomy syndrome is a pathological condition consisting in a persistent violation of the function of external respiration caused by the inflection of the trachea and/or the main bronchus and manifested by respiratory insufficiency. PRESENTATION OF CASE The article describes a method of video-assisted thoracoscopic surgery for intraoperative prevention of the development of postpneumonectomy syndrome by installing a mesh implant in a patient with central cancer of the right lung. DISCUSSION The range of alternatives that have been put forth for the treatment and prevention of the emergence of postpneumonectomy syndrome, on the one hand, demonstrates the relevance of the issue and, on the other, demonstrates the inadequate outcomes of the employed techniques. CONCLUSION As a result of a mesh implant that is rigidly linked to the ribs, the preventative strategy we describe reliably prevents mediastinal displacement and doesn't necessitate repeated surgical intervention.
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Affiliation(s)
- Andrey Nikulin
- City Clinical Hospital No. 23 named after I. V. Davydovsky of Moscow Healthcare Department, Yauzskaya str. 11, Moscow 119027, Russia
| | - Gamzat Diduev
- City Clinical Hospital No. 23 named after I. V. Davydovsky of Moscow Healthcare Department, Yauzskaya str. 11, Moscow 119027, Russia
| | - Arkady Romanikhin
- City Clinical Hospital No. 23 named after I. V. Davydovsky of Moscow Healthcare Department, Yauzskaya str. 11, Moscow 119027, Russia
| | - Tatyana Khorobrykh
- Department of Faculty Surgery No. 2, I.M. Sechenov First Moscow State Medical University (Sechenov University), Dovator Street 15, Moscow 119048, Russia
| | - Ivan Ivashov
- Department of Faculty Surgery No. 2, I.M. Sechenov First Moscow State Medical University (Sechenov University), Dovator Street 15, Moscow 119048, Russia.
| | - Thakur Ajay Singh
- Department of Faculty Surgery No. 2, I.M. Sechenov First Moscow State Medical University (Sechenov University), Dovator Street 15, Moscow 119048, Russia
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Comrie ML, Monteith G, Zur Linden A, Oblak M, Phillips J, James FMK. The accuracy of computed tomography scans for rapid prototyping of canine skulls. PLoS One 2019; 14:e0214123. [PMID: 30908536 PMCID: PMC6433237 DOI: 10.1371/journal.pone.0214123] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 03/08/2019] [Indexed: 12/27/2022] Open
Abstract
This study’s objective was to determine the accuracy of using current computed tomography (CT) scan and software techniques for rapid prototyping by quantifying the margin of error between CT models and laser scans of canine skull specimens. Twenty canine skulls of varying morphology were selected from an anatomy collection at a veterinary school. CT scans (bone and standard algorithms) were performed for each skull, and data segmented (testing two lower threshold settings of 226HU and -650HU) into 3-D CT models. Laser scans were then performed on each skull. The CT models were compared to the corresponding laser scan to determine the error generated from the different types of CT model parameters. This error was then compared between the different types of CT models to determine the most accurate parameters. The mean errors for the 226HU CT models, both bone and standard algorithms, were not significant from zero error (p = 0.1076 and p = 0.0580, respectively). The mean errors for both -650HU CT models were significant from zero error (p < 0.001). Significant differences were detected between CT models for 3 CT model comparisons: Bone (p < 0.0001); Standard (p < 0.0001); and -650HU (p < 0.0001). For 226HU CT models, a significant difference was not detected between CT models (p = 0.2268). Independent of the parameters tested, the 3-D models derived from CT imaging accurately represent the real skull dimensions, with CT models differing less than 0.42 mm from the real skull dimensions. The 226HU threshold was more accurate than the -650HU threshold. For the 226HU CT models, accuracy was not dependent on the CT algorithm. For the -650 CT models, bone was more accurate than standard algorithms. Knowing the inherent error of this procedure is important for use in 3-D printing for surgical planning and medical education.
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Affiliation(s)
- Michaela L. Comrie
- Department Human Health and Nutritional Science, College of Biological Science, University of Guelph, Guelph, Ontario, Canada
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Gabrielle Monteith
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Alex Zur Linden
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Michelle Oblak
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - John Phillips
- Centre for Advanced Manufacturing and Design Technologies, Sheridan College, Brampton, Ontario, Canada
| | - Fiona M. K. James
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
- * E-mail:
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Thompson A, McNally D, Maskery I, Leach RK. X-ray computed tomography and additive manufacturing in medicine: a review. INTERNATIONAL JOURNAL OF METROLOGY AND QUALITY ENGINEERING 2017. [DOI: 10.1051/ijmqe/2017015] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Johnson BN, Lancaster KZ, Zhen G, He J, Gupta MK, Kong YL, Engel EA, Krick KD, Ju A, Meng F, Enquist LW, Jia X, McAlpine MC. 3D Printed Anatomical Nerve Regeneration Pathways. ADVANCED FUNCTIONAL MATERIALS 2015; 25:6205-6217. [PMID: 26924958 PMCID: PMC4765385 DOI: 10.1002/adfm.201501760] [Citation(s) in RCA: 179] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
An imaging-coupled 3D printing methodology for the design, optimization, and fabrication of a customized nerve repair technology for complex injuries is presented. The custom scaffolds are deterministically fabricated via a microextrusion printing principle which enables the simultaneous incorporation of anatomical geometries, biomimetic physical cues, and spatially controlled biochemical gradients in a one-pot 3D manufacturing approach.
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Affiliation(s)
- Blake N. Johnson
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States, Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Karen Z. Lancaster
- Department of Molecular Biology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, United States
| | - Gehua Zhen
- Department of Orthopedic Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Junyun He
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States
| | - Maneesh K. Gupta
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Yong Lin Kong
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Esteban A. Engel
- Department of Molecular Biology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, United States
| | - Kellin D. Krick
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Alex Ju
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Fanben Meng
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Lynn W. Enquist
- Department of Molecular Biology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, United States
| | - Xiaofeng Jia
- Department of Neurosurgery, Orthopedics, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States, Department of Biomedical Engineering, Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Michael C. McAlpine
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States, Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, United States
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