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Using CT radiomic features based on machine learning models to subtype adrenal adenoma.

Shouliang Qi^1,2, Yifan Zuo^1,2, Runsheng Chang^1,2

¹College of Medicine and Biological Information Engineering, Northeastern University, 110169, Shenyang, China.

|February 1, 2023

Summary

This study uses computed tomography (CT) radiomic features and machine learning to differentiate functioning from non-functioning adrenal adenoma, offering a non-invasive diagnostic approach.

Keywords:

Adrenal adenoma Computed tomography Machine learning Radiomic features Radiomics

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Endocrinology

Background:

Adrenocortical adenomas present as functioning or non-functioning subtypes, necessitating accurate differential diagnosis.
Current diagnostic methods, such as adrenal venous sampling, are invasive and require endocrinologic assessment.

Purpose of the Study:

To develop a non-invasive model for differentiating functioning and non-functioning adrenocortical adenomas.
To leverage computed tomography (CT) radiomic features and machine learning (ML) for adenoma subtyping.

Main Methods:

Extracted 1,967 radiomic features from non-contrast, arterial, and venous phase CT images of 289 adrenal adenoma patients.
Selected ten discriminative features per phase or combined phases and built prediction models using various ML algorithms.
Validated models using an external dataset of 54 patients.

Main Results:

Logistic regression (LR) achieved the highest accuracy (83.0%) when combining radiomic features from three CT phases.
External validation in Dataset 2 showed LR achieving the highest accuracy at 77.8%.
Adding clinical information improved the area under the receiver operating characteristic curve for most ML methods.

Conclusions:

CT-derived radiomic features can potentially distinguish functioning from non-functioning adrenal adenomas.
The developed radiomic models offer a non-invasive, cost-effective, and rapid alternative to invasive testing for incidentally discovered adrenal adenomas.