Machine learning applications in gynecological cancer: A critical review.

Journal: Critical reviews in oncology/hematology
Published Date: Sep 7, 2022

Abstract

Machine Learning (ML) represents a computer science capable of generating predictive models, by exposure to raw, training data, without being rigidly programmed. Over the last few years, ML has gained attention within the field of oncology, with considerable strides in both diagnostic, predictive, and prognostic spectrum of malignancies, but also as a catalyst of cancer research. In this review, we discuss the state of ML applications on gynecologic oncology and systematically address major technical and ethical concerns, with respect to their real-world medical practice translation. Undoubtedly, advances in ML will enable the analysis of large, rather complex, datasets for improved, cost-effective, and efficient clinical decisions.

Authors

  • Oraianthi Fiste
    Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece. Electronic address: ofiste@med.uoa.gr.
  • Michalis Liontos
    Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece.
  • Flora Zagouri
    Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece.
  • Georgios Stamatakos
    In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
  • Meletios Athanasios Dimopoulos
    Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece.

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