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01-04-2022 | Meningioma | Review

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

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

Published in: Neuroradiology | Issue 4/2022

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.

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Title: Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis
Authors: Kelvin Koong
Veronica Preda
Anne Jian
Benoit Liquet-Weiland
Antonio Di Ieva
Publication date: 01-04-2022
Publisher: Springer Berlin Heidelberg
Keywords: Meningioma
Pituitary Adenoma
Craniopharyngioma
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Artificial Intelligence
Meningioma
Brain Tumor
Published in: Neuroradiology / Issue 4/2022
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI: https://doi.org/10.1007/s00234-021-02845-1

2024 ASCO Annual Meeting

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusion

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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