Machine learning in metastatic cancer research : Potentials, possibilities, and prospects

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Saeed Faisal
  • Shadi Basurra
  • Ibukun Omotayo Muyide
  • Xiangtao Li
  • Qiuzhen Lin

Detail(s)

Original languageEnglish
Pages (from-to)2454-2470
Journal / PublicationComputational and Structural Biotechnology Journal
Volume21
Online published29 Mar 2023
Publication statusPublished - 2023

Link(s)

Abstract

Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner. © 2023 The Author(s).

Research Area(s)

  • Cancer metastasis, Data inequality, Deep learning, Early detection, Machine learning, Metastatic cancer

Citation Format(s)

Machine learning in metastatic cancer research: Potentials, possibilities, and prospects. / Petinrin, Olutomilayo Olayemi; Faisal, Saeed; Toseef, Muhammad et al.
In: Computational and Structural Biotechnology Journal, Vol. 21, 2023, p. 2454-2470.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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