Keywords: Radiomics, Radiomics
Motivation: Pituitary adenomas (PAs) are a rare but clinically diverse group of tumors with varying hormone secretion profiles and clinical characteristics, comprising 15% of intracranial tumors. Typical classification of PAs relies on blood hormone levels as gold standard test, with a limited exploration into assessing hormone status using neuroimaging biomarkers.
Goal(s): We aim to offer a practical MRI-based classification model, improving clinical PA management.
Approach: Our study developed a machine learning model using MRI radiomics as image biomarkers for the classification of PAs focusing on six subtypes.
Results: Our SVM model showed an accuracy of 0.65 based on MRI images.
Impact: Our radiomics classification model promises to revolutionize MRI PA classification and diagnosis, enhancing clinical management and benefiting scientists, clinicians, and patients by enabling more accurate and efficient diagnostics and treatments.
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