RT journal article
T1 Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis
A1 Lobato-Delgado, Bárbara
A1 Priego Torres, Blanca María
A1 Sánchez Morillo, Daniel
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 cancer
K1 survival analysis
K1 prognosis prediction
K1 patient risk stratification
K1 multimodal data
K1 data integration
K1 Artificial Intelligence
K1 machine learning
AB Cancer is one of the most detrimental diseases globally. Accordingly, the prognosisprediction of cancer patients has become a field of interest. In this review, we have gathered 43 stateof-the-art scientific papers published in the last 6 years that built cancer prognosis predictive modelsusing 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 informationthat each modality provides. The 43 studies were divided into three categories based on the modellingapproach taken, and their characteristics were further discussed together with current issues andfuture trends. Research in this area has evolved from survival analysis through statistical modellingusing mainly clinical and anatomopathological data to the prediction of cancer prognosis through amulti-faceted data-driven approach by the integration of complex, multimodal, and high-dimensionaldata containing multi-omics and medical imaging information and by applying Machine Learningand, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictivemultimodal models are capable of better stratifying patients, which can improve clinical managementand contribute to the implementation of personalised medicine as well as provide new and valuableknowledge on cancer biology and its progression.
PB MDPI
SN 2072-6694
YR 2022
FD 2022-07
LK http://hdl.handle.net/10498/27603
UL http://hdl.handle.net/10498/27603
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026