363-05-010 ISMRM Abstract

Deep learning MRI model for transcription factor-based classification of pituitary neuroendocrine tumor subtypes

Primary: Analysis Methods - Classification and Prediction

Secondary: Neuro - Tumors

363-05-010 · Classification and Analysis in the Brain · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row D

Keywords: MRI Brain Tumor classification Pituitary neuroendocrine tumor Ensemble deep learning Transcription factor

Accepted

Elizabeth N Ndimulunde ¹, Bing-Fong Lin², Dao-Chen Lin³, Chia-Feng Lu¹

¹Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan

²Biomedical Imaging and Radiological Sciences, China Medical University, Taichung, Taiwan

³Department of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan

Presenting Author: Elizabeth N Ndimulunde

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References

1. Daly, A.F. and A. Beckers, The Epidemiology of Pituitary Adenomas. Endocrinol Metab Clin North Am, 2020. 49(3): p. 347-355. [PMID: 32741475] [pmid]

2. Asa, S.L., et al., Overview of the 2022 WHO Classification of Pituitary Tumors. Endocr Pathol, 2022. 33(1): p. 6-26.[PMID: 35291028] [pmid]

3. Sood, R., et al., Transcription Factor Immunohistochemistry in the Classification of Pituitary Neuroendocrine Tumor/Adenoma: Proposal in a Limited-Resource Setting. Arch Pathol Lab Med, 2024. 148(2): p. 178-189. [PMID: 37074863] [pmid]

4. Bing-Fong Lin, D.-C.L., and Chia-Feng Lu. Deep-learning auto-segmentation and subtype classification of pituitary adenoma based on MRI radiomics. in International Society of Magnetic Resonance in Medicine. 2023. Toronto, Canada.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/363-05-010