Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis
Por:
Lobato-Delgado, B, Priego-Torres, B, Sanchez-Morillo, D
Publicada:
1 jul 2022
Resumen:
Simple Summary The rise of Big Data, the widespread use of Machine Learning, and the cheapening of omics techniques have allowed for the creation of more sophisticated and accurate models in biomedical research. This article presents the state-of-the-art predictive models of cancer prognosis that use multimodal data, considering clinical, molecular (omics and non-omics), and image data. The subject of study, the data modalities used, the data processing and modelling methods applied, the validation strategies involved, the integration strategies encompassed, and the evolution of prognostic predictive models are discussed. Finally, we discuss challenges and opportunities in this field of cancer research, with great potential impact on the clinical management of patients and, by extension, on the implementation of personalised and precision medicine. 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.
Filiaciones:
Lobato-Delgado, B:
Hospital Santa Creu & St Pau, Unitat Genom Malalties Complexes, IIB St Pau, Inst Recerca, Barcelona 08041, Spain
Priego-Torres, B:
Univ Cadiz, Dept Automat Engn Elect & Comp Architecture & Net, Cadiz 11519, Spain
Univ Cadiz, Biomed Engn & Telemed Res Grp, Cadiz 11519, Spain
Inst Invest & Innovac Biomed Cadiz INiBICA, Cadiz 11009, Spain
Sanchez-Morillo, D:
Univ Cadiz, Dept Automat Engn Elect & Comp Architecture & Net, Cadiz 11519, Spain
Univ Cadiz, Biomed Engn & Telemed Res Grp, Cadiz 11519, Spain
Inst Invest & Innovac Biomed Cadiz INiBICA, Cadiz 11009, Spain
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