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Novel and Innovative Hybrid Technique for Type A Aortic Dissection

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Inter-observer variability of expert-derived morphologic risk predictors in aortic dissection.

Martin J Willemink¹, Domenico Mastrodicasa^1,2, Mohammad H Madani¹

¹Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

European Radiology

|August 27, 2022

Summary

This summary is machine-generated.

Standardized training and workflows significantly improved the reproducibility of manual measurements of aortic dissection on CT angiography (CTA) scans. This enhanced reliability is crucial for developing accurate machine learning models.

Keywords:

Aortic dissection Computed tomography angiography Variability, inter-observer

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Area of Science:

Radiology
Medical Imaging Analysis
Cardiovascular Imaging

Background:

Reproducibility of expert measurements on computed tomography angiography (CTA) exams of aortic dissection is critical for establishing ground truth.
Machine learning models for aortic dissection analysis require reliable, expert-derived measurements.

Purpose of the Study:

To assess the reproducibility of expert measurements of morphologic risk predictors in Stanford type B aortic dissection on CTA.
To determine if standardized workflows and training improve inter-observer reliability for these measurements.

Main Methods:

Four observers evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection.
Reproducibility of four morphologic risk predictors was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots.
Inter-observer variability was evaluated with and without standardized workflows and specialized training.

Main Results:

Initial measurements without standardization showed poor to moderate inter-observer agreement (ICCs ranging from 0.04 to 0.68).
After standardized training, inter-observer agreement substantially improved, with ICCs ranging from 0.69 to 0.97.
Bland-Altman analysis confirmed reduced bias and improved limits of agreement post-training.

Conclusions:

Manual measurement of aortic CTA features can achieve high inter-observer reliability through optimization.
Standardized workflows and training are essential for improving the reproducibility of these measurements.
Reliable, manually obtained ground truth labels are vital for developing robust machine learning models for aortic dissection.