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Cushing Syndrome I: Introduction

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Cushing syndrome refers to the collection of clinical manifestations that arise when tissues are exposed to excessive amounts of cortisol or cortisol-like medications over an extended period. Cortisol, a glucocorticoid produced by the adrenal cortex, regulates metabolism, immune responses, and the body’s adaptation to stress. When its concentration remains chronically elevated, these physiological pathways become dysregulated, resulting in the characteristic features of the syndrome.Exogenous...

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Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach.

Yue Long¹, Jie Ren², FuChao Cheng¹

¹College of Computer, Chengdu University, Chengdu, China.

Medical Physics

|April 1, 2024

Summary

This study introduces an interpretable machine learning (ML) framework to analyze brain alterations in Cushing's Disease (CD). The ML approach successfully identified key brain regions and their correlations with clinical symptoms, offering potential for improved diagnosis.

Keywords:

Cushing's disease aging classification gray matter alteration machine learning searchlight technique structural MRI

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Area of Science:

Neuroimaging
Machine Learning
Endocrinology

Background:

Cushing's Disease (CD) involves excessive adrenocorticotrophic hormone secretion, causing significant brain alterations detectable by MRI.
Traditional statistical analysis of MRI in CD has limitations in predicting individual patient outcomes.

Purpose of the Study:

To develop and validate an interpretable machine learning (ML) framework for comprehensive analysis of structural MRI data in Cushing's Disease.
To assess model performance, identify predictive brain regions, and correlate these with clinical symptoms.

Main Methods:

An interpretable ML framework was applied to structural MRI data from patients with Cushing's Disease.
The framework included model-level assessment for brain region identification, feature-level assessment for predictive accuracy, and biology-level assessment for clinical correlation.

Main Results:

Significant alterations in brain regions including the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and Inferior frontal gyrus were identified in CD patients.
Correlations were found between clinical symptoms and frontotemporal lobes, insula, and olfactory cortex, consistent with prior research.

Conclusions:

The proposed ML framework demonstrates significant potential for elucidating the pathophysiological mechanisms of Cushing's Disease.
This approach may have broader applications in diagnosing other neurological and endocrine disorders.