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Related Experiment Video

Updated: Sep 29, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Discrimination between non-functioning pituitary adenomas and hypophysitis using machine learning methods based on

Serdar Sahin¹, Gokcen Yildiz², Seda Hanife Oguz³

¹Division of Endocrinology and Metabolism, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.

|March 25, 2022

Summary

Machine learning effectively differentiates hypophysitis from non-functioning pituitary adenomas using radiomic features. Support vector machines demonstrated superior performance in this diagnostic task.

Keywords:

Hypophysitis Machine learning Non-functioning pituitary adenoma, support vector machines

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Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Hypophysitis involves pituitary gland inflammation, causing mass effects and hormonal issues.
Differentiating hypophysitis from non-functioning pituitary adenomas is clinically important.

Purpose of the Study:

To assess machine learning's role in distinguishing hypophysitis from non-functioning pituitary adenomas.
To identify key radiomic features for differential diagnosis.

Main Methods:

Radiomic parameters from T1-weighted contrast-enhanced MRI were analyzed.
Feature selection reduced correlated parameters, focusing on distinguishing features.
Machine learning algorithms utilized gray-level run-length matrix, gray-level co-occurrence matrix-correlation, and gray-level co-occurrence entropy.

Main Results:

The study included 34 patients (17 hypophysitis, 17 non-functioning pituitary adenomas).
Ten radiomic features were identified as discriminative.
Support vector machines achieved the highest accuracy in differentiating the two conditions.

Conclusions:

Machine learning, particularly support vector machines, shows significant potential in differentiating hypophysitis from non-functioning pituitary adenomas.
Radiomic analysis provides valuable insights for improving diagnostic accuracy in pituitary lesions.