A deep learning approach for overall survival prediction in lung cancer with missing values.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 28, 2024

Abstract

BACKGROUND AND OBJECTIVE: In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges.

Authors

  • Camillo Maria Caruso
    Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy. Electronic address: camillomaria.caruso@unicampus.it.
  • Valerio Guarrasi
    Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy. Electronic address: valerio.guarrasi@unicampus.it.
  • Sara Ramella
    Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Italy.
  • Paolo Soda
    Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå, University, Umeå, Sweden. Electronic address: paolo.soda@umu.se.

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