Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration

dc.contributor.authorFetah, Kirsten Lee
dc.contributor.authorDiPardo, Benjamin J.
dc.contributor.authorKongadzem, Eve-Mary
dc.contributor.authorTomlinson, James S.
dc.contributor.authorElzagheid, Adam
dc.contributor.authorElmusrati, Mohammed
dc.contributor.authorKhademhosseini, Ali
dc.contributor.authorAshammakhi, Nureddin
dc.contributor.departmentDigital Economy-
dc.contributor.facultyfi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations|-
dc.contributor.orcidhttps://orcid.org/0000-0001-9304-6590-
dc.contributor.organizationfi=Vaasan yliopisto|en=University of Vaasa|
dc.date.accessioned2019-11-21T11:25:46Z
dc.date.accessioned2025-06-25T12:25:17Z
dc.date.available2020-11-13T01:00:13Z
dc.date.issued2019-11-13
dc.description.abstractCancer is one of the leading causes of death worldwide, despite the large efforts to improve the understanding of cancer biology and development of treatments. The attempts to improve cancer treatment are limited by the complexity of the local milieu in which cancer cells exist. The tumor microenvironment (TME) consists of a diverse population of tumor cells and stromal cells with immune constituents, microvasculature, extracellular matrix components, and gradients of oxygen, nutrients, and growth factors. The TME is not recapitulated in traditional models used in cancer investigation, limiting the translation of preliminary findings to clinical practice. Advances in 3D cell culture, tissue engineering, and microfluidics have led to the development of “cancer‐on‐a‐chip” platforms that expand the ability to model the TME in vitro and allow for high‐throughput analysis. The advances in the development of cancer‐on‐a‐chip platforms, implications for drug development, challenges to leveraging this technology for improved cancer treatment, and future integration with artificial intelligence for improved predictive drug screening models are discussed.-
dc.description.reviewstatusfi=vertaisarvioitu|en=peerReviewed|-
dc.embargo.lift2020-11-13
dc.embargo.terms2020-11-13
dc.format.bitstreamtrue
dc.format.contentfi=kokoteksti|en=fulltext|-
dc.format.extent14-
dc.identifier.olddbid10727
dc.identifier.oldhandle10024/9946
dc.identifier.urihttps://osuva.uwasa.fi/handle/11111/205
dc.identifier.urnURN:NBN:fi-fe2019112143551-
dc.language.isoeng-
dc.publisherWiley-
dc.relation.doi10.1002/smll.201901985-
dc.relation.ispartofjournalSmall-
dc.relation.issn1613-6829-
dc.relation.issn1613-6810-
dc.relation.urlhttps://doi.org/10.1002/smll.201901985-
dc.source.identifierhttps://osuva.uwasa.fi/handle/10024/9946
dc.subject.disciplinefi=Tietoliikennetekniikka|en=Telecommunications Engineering|-
dc.titleCancer Modeling-on-a-Chip with Future Artificial Intelligence Integration-
dc.type.okmfi=A2 Katsausartikkeli tieteellisessä aikakauslehdessä|en=A2 Peer-reviewed review article|sv=A2 Översiktsartikel i en vetenskaplig tidskrift|-
dc.type.publicationarticle-

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