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A multimodal ensemble driven by multiobjective optimisation to predict overall survival in non-small-cell lung cancer

Caruso, Camillo Maria (författare)
Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
Guarrasi, Valerio (författare)
Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Roma, Italy
Cordelli, Ermanno (författare)
Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
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Sicilia, Rosa (författare)
Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
Gentile, Silvia (författare)
Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario, Campus Bio-Medico, Via Alvaro del Portillo, Roma, Italy
Messina, Laura (författare)
Operative Research Unit of Diagnostic Imaging, Fondazione Policlinico Universitario, Campus Bio-Medico, Via Alvaro del Portillo, 200, Roma, Italy
Fiore, Michele (författare)
Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario, Campus Bio-Medico, Via Alvaro del Portillo, Roma, Italy; Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
Piccolo, Claudia (författare)
Operative Research Unit of Diagnostic Imaging, Fondazione Policlinico Universitario, Campus Bio-Medico, Via Alvaro del Portillo, 200, Roma, Italy
Beomonte Zobel, Bruno (författare)
Operative Research Unit of Diagnostic Imaging, Fondazione Policlinico Universitario, Campus Bio-Medico, Via Alvaro del Portillo, 200, Roma, Italy; Research Unit of Diagnostic Imaging, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
Iannello, Giulio (författare)
Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
Ramella, Sara (författare)
Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario, Campus Bio-Medico, Via Alvaro del Portillo, Roma, Italy; Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
Soda, Paolo (författare)
Umeå universitet,Radiofysik,Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21, Roma, Italy
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 (creator_code:org_t)
2022-11-02
2022
Engelska.
Ingår i: Journal of Imaging. - : MDPI. - 2313-433X. ; 8:11
Relaterad länk:
https://doi.org/10.3...
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https://umu.diva-por... (primary) (Raw object)
https://urn.kb.se/re...
https://doi.org/10.3...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Lung cancer accounts for more deaths worldwide than any other cancer disease. In order to provide patients with the most effective treatment for these aggressive tumours, multimodal learning is emerging as a new and promising field of research that aims to extract complementary information from the data of different modalities for prognostic and predictive purposes. This knowledge could be used to optimise current treatments and maximise their effectiveness. To predict overall survival, in this work, we investigate the use of multimodal learning on the CLARO dataset, which includes CT images and clinical data collected from a cohort of non-small-cell lung cancer patients. Our method allows the identification of the optimal set of classifiers to be included in the ensemble in a late fusion approach. Specifically, after training unimodal models on each modality, it selects the best ensemble by solving a multiobjective optimisation problem that maximises both the recognition performance and the diversity of the predictions. In the ensemble, the labels of each sample are assigned using the majority voting rule. As further validation, we show that the proposed ensemble outperforms the models learning a single modality, obtaining state-of-the-art results on the task at hand.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Nyckelord

convolutional neural networks
medical imaging
multiexpert systems
multimodal deep learning
oncology
optimisation
precision medicine
tabular data

Publikations- och innehållstyp

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