Subtype classification of Functional Pituitary Adenomas based on MRI 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.