From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction

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dc.contributor.authorFarinella, Riccardo
dc.contributor.authorFelici, Alessio
dc.contributor.authorPeduzzi, Giulia
dc.contributor.authorTestoni, Sabrina Gloria Giulia
dc.contributor.authorCostello, Eithne
dc.contributor.authorAretini, Paolo
dc.contributor.authorBlazquez\\-Encinas, Ricardo
dc.contributor.authorOz, Elif
dc.contributor.authorPastore, Aldo
dc.contributor.authorTacelli, Matteo
dc.contributor.authorOtlu, Burcak
dc.contributor.authorCampa, Daniele
dc.contributor.authorGentiluomo, Manuel
dc.date.accessioned2025-10-16T15:11:54Z
dc.date.issued2025
dc.identifier.doi10.1016/j.semcancer.2025.03.004
dc.identifier.otherWOS:001460315500001
dc.identifier.urihttps://openaccess.acibadem.edu.tr/handle/11443/5104
dc.publisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.sourceSEMINARS IN CANCER BIOLOGY
dc.subjectPancreatic cancer
dc.subjectRisk stratification
dc.subjectArtificial intelligence
dc.subjectGenetic susceptibility
dc.subjectEarly detection
dc.titleFrom classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction
dc.typeArticle

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