BEEx Is an Open-Source Tool That Evaluates Batch Effects in Medical Images to Enable Multicenter Studies

Area of Science:

• Computational pathology
• Medical imaging analysis
• Artificial intelligence in healthcare

Background:

• Batch effects introduce nonbiological variations in medical image datasets due to differing acquisition parameters.
• These variations can compromise the accuracy and generalizability of computational pathology and radiology models, particularly in multicenter research.

Purpose of the Study:

• To develop an open-source platform, Batch Effect Explorer (BEEx), for identifying and quantifying batch effects in medical imaging data.
• To provide researchers with tools to assess the impact of batch effects on multicenter studies.

Main Methods:

• BEEx integrates visualization and quantitative metrics (intensity, gradient, texture) for unsupervised batch effect detection.
• The platform supports diverse medical imaging modalities, including microscopy and radiology.
• Four use cases demonstrated BEEx's capability in identifying batch effects and validating mitigation strategies.

Main Results:

• BEEx successfully identified batch effects across different medical imaging datasets.
• The tool's metrics and visualizations effectively distinguished datasets originating from different sites or scanners.
• Validation through use cases confirmed the platform's utility in assessing batch effect magnitude.

Conclusions:

• BEEx is a scalable and versatile framework for detecting and analyzing batch effects in medical images.
• This prescreening tool enhances the reliability and validity of image-based studies and facilitates the development of robust AI-based cancer diagnostic models.