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      Methods of 3D printing models of pituitary tumors

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          Abstract

          Background

          Pituitary adenomas can give rise to a variety of clinical disorders and surgery is often the primary treatment option. However, preoperative magnetic resonance imaging (MRI) does not always reliably identify the site of an adenoma. In this setting molecular (functional) imaging (e.g. 11C-methionine PET/CT) may help with tumor localisation, although interpretation of these 2D images can be challenging. 3D printing of anatomicalal models for other indications has been shown to aid surgical planning and improve patient understanding of the planned procedure. Here, we explore the potential utility of four types of 3D printing using PET/CT and co-registered MRI for visualising pituitary adenomas.

          Methods

          A 3D patient-specific model based on a challenging clinical case was created by segmenting the pituitary gland, pituitary adenoma, carotid arteries and bone using contemporary PET/CT and MR images. The 3D anatomical models were printed using VP, MEX, MJ and PBF 3D printing methods. Different anatomicalal structures were printed in color with the exception of the PBF anatomical model where a single color was used. The anatomical models were compared against the computer model to assess printing accuracy. Three groups of clinicians (endocrinologists, neurosurgeons and ENT surgeons) assessed the anatomical models for their potential clinical utility.

          Results

          All of the printing techniques produced anatomical models which were spatially accurate, with the commercial printing techniques (MJ and PBF) and the consumer printing techniques (VP and MEX) demonstrating comparable findings (all techniques had mean spatial differences from the computer model of < 0.6 mm). The MJ, VP and MEX printing techniques yielded multicolored anatomical models, which the clinicians unanimously agreed would be preferable to use when talking to a patient; in contrast, 50%, 40% and 0% of endocrinologists, neurosurgeons and ENT surgeons respectively would consider using the PBF model.

          Conclusion

          3D anatomical models of pituitary tumors were successfully created from PET/CT and MRI using four different 3D printing techniques. However, the expert reviewers unanimously preferred the multicolor prints. Importantly, the consumer printers performed comparably to the commercial MJ printing technique, opening the possibility that these methods can be adopted into routine clinical practice with only a modest investment.

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          Most cited references28

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          3D Slicer as an image computing platform for the Quantitative Imaging Network.

          Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Medical 3D Printing for the Radiologist.

            While use of advanced visualization in radiology is instrumental in diagnosis and communication with referring clinicians, there is an unmet need to render Digital Imaging and Communications in Medicine (DICOM) images as three-dimensional (3D) printed models capable of providing both tactile feedback and tangible depth information about anatomic and pathologic states. Three-dimensional printed models, already entrenched in the nonmedical sciences, are rapidly being embraced in medicine as well as in the lay community. Incorporating 3D printing from images generated and interpreted by radiologists presents particular challenges, including training, materials and equipment, and guidelines. The overall costs of a 3D printing laboratory must be balanced by the clinical benefits. It is expected that the number of 3D-printed models generated from DICOM images for planning interventions and fabricating implants will grow exponentially. Radiologists should at a minimum be familiar with 3D printing as it relates to their field, including types of 3D printing technologies and materials used to create 3D-printed anatomic models, published applications of models to date, and clinical benefits in radiology. Online supplemental material is available for this article.
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              R: a language and environment for statistical computing

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                Author and article information

                Contributors
                dg538@medschl.cam.ac.uk
                Journal
                3D Print Med
                3D Print Med
                3D Printing in Medicine
                Springer International Publishing (Cham )
                2365-6271
                31 August 2021
                31 August 2021
                December 2021
                : 7
                : 24
                Affiliations
                [1 ]GRID grid.24029.3d, ISNI 0000 0004 0383 8386, Department of Nuclear Medicine, , Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, ; Hills Road, Cambridge, CB2 0QQ UK
                [2 ]GRID grid.5335.0, ISNI 0000000121885934, Cambridge Endocrine Molecular Imaging Group, , University of Cambridge, Addenbrooke’s Hospital, ; Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ UK
                [3 ]GRID grid.24029.3d, ISNI 0000 0004 0383 8386, Clinical Engineering, , Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, ; Hills Road, Cambridge, CB2 0QQ UK
                [4 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Radiology, , University of Cambridge, Cambridge Biomedical Campus, ; Hills Road, Cambridge, CB2 0QQ UK
                [5 ]GRID grid.5335.0, ISNI 0000000121885934, Division of Neurosurgery, Department of Clinical Neurosciences, , University of Cambridge & Addenbrooke’s Hospital, ; Cambridge, CB2 0QQ UK
                [6 ]GRID grid.5335.0, ISNI 0000000121885934, Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, , University of Cambridge, National Institute for Health Research, Cambridge Biomedical Research Centre, Addenbrooke’s Hospital, ; Hills Road, Cambridge, CB2 0QQ UK
                Author information
                http://orcid.org/0000-0002-9773-6502
                Article
                118
                10.1186/s41205-021-00118-4
                8406959
                34462823
                3b9bf926-4c1f-4e26-923c-cbb0d197582e
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 21 May 2021
                : 15 August 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002927, Addenbrooke's Charitable Trust, Cambridge University Hospitals;
                Award ID: 900159
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                3d printing,pituitary,pet/ct,mri,cost analysis,clinical utility
                3d printing, pituitary, pet/ct, mri, cost analysis, clinical utility

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