Abstract #3604

Deep-learning auto-segmentation and subtype classification of pituitary adenoma based on MRI radiomics

Bing-Fong Lin¹, Dao-Chen Lin^2,3,4, and Chia-Feng Lu¹

¹Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ²Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan, Taipei, Taiwan, ³Division of Endocrine and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ⁴School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

Synopsis

Keywords: Radiomics, Quantitative Imaging, Pituitary adenomaPituitary adenoma (PA) accounts for approximately 15% in intracranial neoplasms. The classification of PA generally based on the hormone level of blood as the gold standard test, while the analysis of hormone condition using neuroimaging biomarkers was less explored. Accordingly, our study developed a model to automatically segment PA and further used the quantitative and non-invasive MRI technique as image biomarkers to classify the three types of hormone pattern, focusing on corticortroph, gonadotroph, and plurihormonal type. We aimed to provide a feasible classification model based MRI to benefit the clinical management of patients with PA.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.