Publication:
Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.

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Date

2022-06-30

Authors

Lobato-Delgado, Barbara
Priego-Torres, Blanca
Sanchez-Morillo, Daniel

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MDPI
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Abstract

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.

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MeSH Terms

Deep learning
Multiomics
Precision medicine
Neoplasms
Machine learning
Diagnostic imaging
Prognosis
Survival analysis

DeCS Terms

Análisis de supervivencia
Aprendizaje automático
Aprendizaje profundo
Diagnóstico por imagen
Medicina de precisión
Multiómica
Neoplasias
Pronóstico

CIE Terms

Keywords

Artificial intelligence, Cancer, Data integration, Machine learning, Multimodal data, Patient risk stratification, Prognosis prediction, Survival analysis

Citation

Lobato-Delgado B, Priego-Torres B, Sanchez-Morillo D. Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis. Cancers (Basel). 2022 Jun 30;14(13):3215