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Abstract #1379

A Deep Learning Ensemble Model for the Classification of Pituitary Neuroendocrine Tumors Subtypes Using Magnetic Resonance Imaging

Elizabeth Nailoke Ndimulunde1, Bing-Fong Lin2, Dao-Chen Lin3, and Chia-Feng Lu4
1Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei City, Taiwan, 2Biomedical Imaging and Radiological Sciences, China Medical University, Taichung, Taiwan, 3Taipei Veteran General Hospital, Taipei City, Taiwan, 4National Yang Ming Chiao Tung University, Taipei City, Taiwan

Synopsis

Keywords: Diagnosis/Prediction, Brain, Pituitary tumor

Motivation: Pituitary Neuroendocrine Tumors (PitNET) are a diverse group, representing 15% of intracranial tumors. Preoperative MRI cannot differentiate PitNET subtypes effectively, requiring patients to undergo surgery before classification is performed.

Goal(s): Current classification relies on clinical manifestations and hormone tests which can be controversial and inconclusive. The study aims to develop a non-invasive MRI-based method to classify PitNET subtypes.

Approach: We developed an MRI-based deep learning ensemble model to classify PitNET subtypes by transcription factors: TPIT, SF1, and PIT1.

Results: Our model achieved an accuracy of 76.92%, with class-specific AUC values of 0.88 for TPIT, 0.96 for SF1, and 0.86 for PIT1.

Impact: Our model provides a non-invasive method for classifying PitNET subtypes using MRI, potentially enhancing the accuracy of preoperative diagnosis beyond reliance on hormone tests alone ultimately improving patient outcomes.

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Keywords