Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis
Lobato-Delgado, Bárbara (Institut d'Investigació Biomèdica Sant Pau)
Priego-Torres, Blanca (Instituto de Investigación e Innovación Biomédica de Cádiz)
Sanchez-Morillo, Daniel (Instituto de Investigación e Innovación Biomédica de Cádiz)

Data:	2022
Resum:	Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.
Nota:	Altres ajuts: Junta de Andalucía (PI-0032-2017); Subvención para la financiación de la investigación y la innovación biomédica y en Ciencias de la Salud en el marco de la iniciativa territorial integrada 2014-2020 para la provincia de Cádiz; Consejería de Salud y Familias; Unión Europea, Fondo de Desarrollo Regional (FEDER); Universidad de Cádiz (PAIDI TIC-212); Plan Propio UCA 2022-2023.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article de revisió ; recerca ; Versió publicada
Matèria:	Cancer ; Survival analysis ; Prognosis prediction ; Patient risk stratification ; Multimodal data ; Data integration ; Artificial Intelligence ; Machine learning
Publicat a:	Cancers, Vol. 14 Núm. 13 (July 2022) , p. 3215, ISSN 2072-6694

DOI: 10.3390/cancers14133215
PMID: 35804988

26 p, 520.5 KB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut de Recerca Sant Pau
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