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REVIEW article
Front. Endocrinol.
Sec. Pituitary Endocrinology
Volume 15 - 2024 |
doi: 10.3389/fendo.2024.1426781
This article is part of the Research Topic Surgery and Management of Pituitary Region Tumours and Their Endocrine Outcomes View all articles
The Current State of MRI-Based Radiomics in Pituitary Adenoma: Promising but Challenging
Provisionally accepted- 1 Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- 2 Department of Neurosurgery, People's Hospital of Biyang County, Henan Province, Zhengzhou, China
- 3 Department of Ophthalmology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hebei Province, China
In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients.
Keywords: Pit-net, pituitary adenoma, Radiomics, textual analysis, machine learning, biomarkers, Neuroimaging
Received: 02 May 2024; Accepted: 30 Aug 2024.
Copyright: © 2024 Zheng, Zhao, Zheng, Liu, Li, Jiang, Huang, Ye and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Youfan Ye, Department of Ophthalmology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hebei Province, China
Haijun Wang, Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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