TY - JOUR
T1 - Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors
T2 - a quantitative and qualitative synthesis
AU - Koong, Kelvin
AU - Preda, Veronica
AU - Jian, Anne
AU - Liquet-Weiland, Benoit
AU - Di Ieva, Antonio
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Deep learning
KW - Machine learning
KW - Magnetic resonance imaging
KW - Pituitary neoplasms
KW - Radiomics
UR - http://www.scopus.com/inward/record.url?scp=85120031608&partnerID=8YFLogxK
U2 - 10.1007/s00234-021-02845-1
DO - 10.1007/s00234-021-02845-1
M3 - Review article
C2 - 34839380
AN - SCOPUS:85120031608
SN - 0028-3940
VL - 64
SP - 647
EP - 668
JO - Neuroradiology
JF - Neuroradiology
IS - 4
ER -