3D Printing in Medicine; Application in Intracranial Tumours in Southern Nigeria

Morgan Ikponmwosa *

Department of General Medicine, William Harvey Hospital, Ashford, Kent, United Kingdom.

Ayodele Olugbenga Ogunsemoyin

Department of Radiology, University of Medical Sciences, Laje Road, Ondo, Ondo State, Nigeria.

Onyekachi Okechukwu Moemenan

Department of Radiology, Digital imaging and diagnostic Centre, Imo state Owerri, Nigeria.

Obinna Ezeigwe

Department of General Medicine, William Harvey Hospital, Ashford, Kent, United Kingdom.

Yaw Barimah Darko

Department of General Medicine, William Harvey Hospital, Ashford, Kent, United Kingdom.

*Author to whom correspondence should be addressed.


Abstract

Background: The pituitary gland is a small bean-shaped gland situated at the base of the brain. Pituitary-based tumors are neuroendocrine tumours affecting the pituitary gland. Imaging of the pituitary gland involves the use of computed tomography and magnetic resonance imaging. 3D reconstruction of data from CT images can be converted into 3D and then made into a live anatomical model using a 3D printer.

The objective and aim of this study are to demonstrate that findings from CT scan images can be used to generate 3D printed specific models for patients and clinicians.

Methods: Patient-specific models for three clinical cases were segmented using a segmentation application to isolate the mass and the bone. The process involved image acquisition from a cross-sectional imaging to segmentation of the acquired DICOM image into a 3D model followed by file and model correction for final print, this is then followed on to slicing with the selection of 3D printing material as well as appropriate settings, this is then concluded with the actual print, print accuracy, and cost analysis.

Results: Segmentation of the region of interest took about 45 to 90 minutes with the majority of the time spent on segmentation of the pituitary. Printing of models was done into sections as the skull and mass were printed separately. The times required spanned from 20-40 minutes and 4-9 hours for the mass and skull base respectively. Print accuracy was less than 1.7mm with the total cost of printing a model was less than $50.

Conclusion: This study showed steps in 3D printing anatomical models from a computed tomogram of patients with brain lesions.

Keywords: 3D printing, accuracy, digital imaging and communications in medicine (DICOM), intracranial tumours, pituitary gland, segmentation


How to Cite

Ikponmwosa, M., Ogunsemoyin, A. O., Moemenan, O. O., Ezeigwe, O., & Darko, Y. B. (2022). 3D Printing in Medicine; Application in Intracranial Tumours in Southern Nigeria. Journal of Advances in Medicine and Medical Research, 34(22), 354–364. https://doi.org/10.9734/jammr/2022/v34i224823

Downloads

Download data is not yet available.

References

John D. Carmichael. Overview of the pituitary gland - hormonal and metabolic disorders [Internet]. MSD Manual Consumer Version. [cited 2022 Aug 30].

Available:https://www.msdmanuals.com/en-gb/home/hormonal-and-metabolic-disorders/pituitary-gland-disorders/overview-of-the-pituitary-gland

Jones J, Datir A. Pituitary gland. Radiopaedia.org [Internet]; 2008 May 2.

[cited 2022 Aug 30]; Available:http://radiopaedia.org/articles/1888

Gaillard F, Weerakkody Y. Pituitary adenoma. Radiopaedia.org [Internet]; 2010 Oct 10.

[cited 2022 Aug 30];

Available:http://radiopaedia.org/articles/11024

Natarajan D, Tatineni S, Ponnapalli SP, Sachdeva V. Pituitary adenoma presenting as acute onset isolated complete third cranial nerve palsy without vision changes. BMJ Case Reports CP [Internet]. 2020 Jun 1 [cited 2022 Aug 30];13(6):e232490.

Available:https://casereports.bmj.com/content/13/6/e232490

Daly AF, Beckers A. The epidemiology of pituitary adenomas. Endocrinol Metab Clin North Am. 2020 Sep 1;49(3):347–55.

Ezzat S, Asa SL, Couldwell WT, Barr CE, Dodge WE, Vance ML, et al. The prevalence of pituitary adenomas: a systematic review. Cancer [Internet]. 2004 Aug 1 [cited 2022 Aug 30];101(3):613–9.

Available:https://pubmed.ncbi.nlm.nih.gov/15274075/

Gillett D, Bashari W, Senanayake R, Marsden D, Koulouri O, MacFarlane J, et al. Methods of 3D printing models of pituitary tumors. 3D Print Med 2021 71 [Internet]. 2021 Aug 31 [cited 2022 Apr 22];7(1):1–14.

Available:https://threedmedprint.biomedcentral.com/articles/10.1186/s41205-021-00118-4

Lake MG, Krook LS, Cruz SV. Pituitary adenomas: An overview. Am Fam Physician [Internet]. 2013 Sep 1;88(5):319–27.

[cited 2022 Aug 30]

Available:https://www.aafp.org/pubs/afp/issues/2013/0901/p319.html

John A. Jane J, Catalino MP, Edward R. Laws J. Surgical treatment of pituitary adenomas. Endotext [Internet]; 2022 Mar 9.

[cited 2022 Aug 30]

Available:https://www.ncbi.nlm.nih.gov/books/NBK278983/

Kamio T, Suzuki M, Asaumi R, Kawai T. DICOM segmentation and STL creation for 3D printing: a process and software package comparison for osseous anatomy. 3D Print Med 2020 61 [Internet]. 2020 Jul 31;6(1):1–12.

[cited 2022 Aug 30]

Available: https://threedmedprint.biomedcentral.com/articles/10.1186/s41205-020-00069-2

Di Prima M, Coburn J, Hwang D, Kelly J, Khairuzzaman A, Ricles L. Additively manufactured medical products – the FDA perspective. 3D Print Med 2016 21 [Internet]. 2016 Dec 1;2(1):1–6.

[cited 2022 Aug 30]

Available:https://threedmedprint.biomedcentral.com/articles/10.1186/s41205-016-0005-9

Volume Viewer | GE Healthcare (United States) [Internet]. [cited 2022 Aug 30].

Available:https://www.gehealthcare.com/products/advanced-visualization/all-applications/volume-viewer

Snikhovska Kseniia. Seven Types of 3D Printers - Different printing and extruder technologies [Internet]; 2022.

[cited 2022 Aug 30].

Available: https://penandplastic.com/3d-printer-types/

Gillett D, Bashari W, Senanayake R, Marsden D, Koulouri O, MacFarlane J, et al. Methods of 3D printing models of pituitary tumors. 3D Print Med 2021 71 [Internet]. 2021 Aug 31;7(1):1–14.

[cited 2022 Aug 30]

Available:https://threedmedprint.biomedcentral.com/articles/10.1186/s41205-021-00118-4

Aimar A, Palermo A, Innocenti B. The role of 3d printing in medical applications: A state of the art. J Healthc Eng. 2019;2019.

Badrigilan S, Nabavi S, Abin AA, Rostampour N, Abedi I, Shirvani A, et al. Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study. Int J Comput Assist Radiol Surg [Internet]. 2021 Apr 1;16(4):529–42.

Available:https://link.springer.com/article/10.1007/s11548-021-02326-z

[cited 2022 Aug 30]

Cost-effectiveness of 3d printing of anatomical models. Veterinaria. 2021;70(Suppl 1).

Petropolis C, Kozan D, Sigurdson L. Accuracy of medical models made by consumer-grade fused deposition modelling printers. Plast Surg [Internet]. 2015;23(2):91.

Available:/pmc/articles/PMC4459415/

[cited 2022 Aug 30]

ERP. How dimensionally accurate are 3D printed parts? - 3ERP [Internet].

[cited 2022 Aug 30].

Available:https://www.3erp.com/blog/how-dimensionally-accurate-are-3d-printed-parts/