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      3D printing applications for the treatment of cancer

      , ,
      Clinical Epidemiology and Global Health
      Elsevier BV

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          Recent advances in 3D printing of biomaterials

          3D Printing promises to produce complex biomedical devices according to computer design using patient-specific anatomical data. Since its initial use as pre-surgical visualization models and tooling molds, 3D Printing has slowly evolved to create one-of-a-kind devices, implants, scaffolds for tissue engineering, diagnostic platforms, and drug delivery systems. Fueled by the recent explosion in public interest and access to affordable printers, there is renewed interest to combine stem cells with custom 3D scaffolds for personalized regenerative medicine. Before 3D Printing can be used routinely for the regeneration of complex tissues (e.g. bone, cartilage, muscles, vessels, nerves in the craniomaxillofacial complex), and complex organs with intricate 3D microarchitecture (e.g. liver, lymphoid organs), several technological limitations must be addressed. In this review, the major materials and technology advances within the last five years for each of the common 3D Printing technologies (Three Dimensional Printing, Fused Deposition Modeling, Selective Laser Sintering, Stereolithography, and 3D Plotting/Direct-Write/Bioprinting) are described. Examples are highlighted to illustrate progress of each technology in tissue engineering, and key limitations are identified to motivate future research and advance this fascinating field of advanced manufacturing.
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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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              Bioprinting for cancer research.

              Bioprinting offers the ability to create highly complex 3D architectures with living cells. This cutting-edge technique has significantly gained popularity and applicability in several fields. Bioprinting methods have been developed to effectively and rapidly pattern living cells, biological macromolecules, and biomaterials. These technologies hold great potential for applications in cancer research. Bioprinted cancer models represent a significant improvement over previous 2D models by mimicking 3D complexity and facilitating physiologically relevant cell-cell and cell-matrix interactions. Here we review bioprinting methods based on inkjet, microextrusion, and laser technologies and compare 3D cancer models with 2D cancer models. We discuss bioprinted models that mimic the tumor microenvironment, providing a platform for deeper understanding of cancer pathology, anticancer drug screening, and cancer treatment development.
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                Author and article information

                Journal
                Clinical Epidemiology and Global Health
                Clinical Epidemiology and Global Health
                Elsevier BV
                22133984
                December 2020
                December 2020
                : 8
                : 4
                : 1072-1076
                Article
                10.1016/j.cegh.2020.03.022
                4107f586-3a89-4b7a-abc0-1dc63595e80e
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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