Evidence map›Paper›PMID 34462823›Full record

Article3D printing in medicine2021

Methods of 3D printing models of pituitary tumors.

Daniel Gillett, Waiel Bashari, Russell Senanayake, Daniel Marsden, Olympia Koulouri, James MacFarlane, Merel van der Meulen, Andrew S Powlson, Iosif A Mendichovszky, Heok Cheow and 4 more

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Article in 3D printing in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Daniel GillettDepartment of Nuclear Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK. dg538@medschl.cam.ac.uk.ORCID http://orcid.org/0000-0002-9773-6502
Waiel BashariCambridge Endocrine Molecular Imaging Group, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Russell SenanayakeCambridge Endocrine Molecular Imaging Group, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Daniel MarsdenClinical Engineering, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Olympia KoulouriCambridge Endocrine Molecular Imaging Group, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
James MacFarlaneCambridge Endocrine Molecular Imaging Group, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Merel van der MeulenCambridge Endocrine Molecular Imaging Group, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Andrew S PowlsonCambridge Endocrine Molecular Imaging Group, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Iosif A MendichovszkyDepartment of Nuclear Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Heok CheowDepartment of Nuclear Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Nick BirdDepartment of Nuclear Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Angelos KoliasDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge & Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK.
Richard MannionDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge & Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK.
Mark GurnellCambridge Endocrine Molecular Imaging Group, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.

Funding

Addenbrooke's Charitable Trust, Cambridge University Hospitals 900159
6 · The paper itself

Abstract

backgroundPituitary 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.

methodsA 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.

resultsAll 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.

conclusion3D 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.

Indexed as

3D printingClinical utilityCost analysisMRIPET/CTPituitary

Identifiers

PMID34462823
PMCPMC8406959

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.