Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.

Journal: Journal of neuro-oncology
PMID: 38689115

Abstract

OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS.

Authors

  • Mustafa M Basree
    Deparment of Human Oncology, University of Wisconsin, Madison, WI, USA.
  • Chengnan Li
    Department of Computer Science, University of Wisconsin, Madison, WI, USA.
  • Hyemin Um
    Department of Radiology, University of Wisconsin, Madison, WI, USA.
  • Anthony H Bui
    Icahn School of Medicine at Mount Sinai.
  • Manlu Liu
    School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
  • Azam Ahmed
    Department of Neurosurgery, University of Wisconsin at Madison, Madison, WI, USA.
  • Pallavi Tiwari
    Department of Radiology, University of Wisconsin, Madison, WI, USA.
  • Alan B McMillan
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705 USA, and also with the Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705 USA.
  • Andrew M Baschnagel
    Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.

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