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Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review

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dc.contributor.authorOróstica, Karen
dc.contributor.authorMardones, Felipe
dc.contributor.authorBernal, Yanara
dc.contributor.authorMolina, Samuel
dc.contributor.authorOrchard, Marcos
dc.contributor.authorVerdugo, Ricardo
dc.contributor.authorCarvajal-Hausdorf, Daniel
dc.contributor.authorMarcelain, Katherine
dc.contributor.authorContreras, Seba
dc.contributor.authorArmisen, Ricardo
dc.date.accessioned2024-12-11T19:45:18Z
dc.date.available2024-12-11T19:45:18Z
dc.date.issued2024
dc.description.abstractCancers of unknown primary (CUP) are a heterogeneous group of aggressive metastatic cancers where standardised diagnostic techniques fail to identify the organ where it originated, resulting in a poor prognosis and resistance to treatment. Recent advances in large-scale sequencing techniques have enabled the identification of mutational signatures specific to particular tumour subtypes, even from liquid biopsy samples such as blood. This breakthrough paves the way for the development of new cost-effective diagnostic strategies. This mini-review explores recent advancements in Machine Learning (ML) and its application to tumour classification methods for CUP patients, identifying its weaknesses and strengths when classifying the tumour type. In the era of multi-omics, integrating several sources of information (e.g., imaging, molecular biomarkers, and family history) requires important theoretical advancements: increasing the dimensionality of the problem can result in lowering the predictive accuracy and robustness when data is scarce. Here, we review and discuss different architectures and strategies for incorporating cutting-edge machine learning into CUP diagnosis, aiming to bridge the gap between theory and clinical practice.
dc.description.versionVersión Aceptada
dc.identifier.citationOróstica K, Mardones F, Bernal YA, Molina S, Orchard M, Verdugo RA, Carvajal-Hausdorf D, Marcelain K, Contreras S, Armisen R. Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review. Cancer Lett. 2024 Nov 28;611:217348. doi: 10.1016/j.canlet.2024.217348
dc.identifier.doihttps://doi.org/10.1016/j.canlet.2024.217348
dc.identifier.urihttps://hdl.handle.net/11447/9471
dc.language.isoen
dc.subjectCancers of unknown primary (CUP)
dc.subjectDiagnostic methods
dc.subjectMachine learning (ML)
dc.subjectMutational signatures
dc.subjectSomatic mutations
dc.subjectTumour classification
dc.titleAdvances in machine learning for tumour classification in cancer of unknown primary: A mini-review
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourceCancer letters
dspace.entity.typePublication
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relation.isAuthorOfPublicationf814e5ac-2623-4a1f-bc2b-5a1a260ee316
relation.isAuthorOfPublication.latestForDiscovery8e2ee03b-003a-4e24-b577-290a5cd449c1

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