Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis

Kelvin Koong, Veronica Preda, Anne Jian, Benoit Liquet-Weiland, Antonio Di Ieva*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Purpose: To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. Methods: PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. Results: Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. Conclusion: This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.

Original languageEnglish
Pages (from-to)647-668
Number of pages22
JournalNeuroradiology
Volume64
Issue number4
Early online date27 Nov 2021
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Magnetic resonance imaging
  • Pituitary neoplasms
  • Radiomics

Fingerprint

Dive into the research topics of 'Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis'. Together they form a unique fingerprint.

Cite this