Standardization of imaging methods for machine learning in neuro-oncology

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence in Medicine

Background:

• Radiomics extracts quantitative imaging features for medical analysis.
• Machine learning facilitates the analysis of large radiomic datasets.
• Radiomics shows potential for neuro-oncology applications.

Purpose of the Study:

• To highlight the importance of standardization in radiomics.
• To review current standardization efforts in neuro-oncological imaging.
• To discuss challenges and potential drawbacks of standardization.

Main Methods:

• Review of existing literature on radiomics and standardization in neuro-oncology.
• Discussion of methodological and reporting standardization needs.
• Analysis of challenges in implementing standardized radiomic protocols.

Main Results:

• Many radiomic tools for neuro-oncology lack clinical implementation due to poor reproducibility.
• Lack of standardization across institutions is a major limitation.
• Standardization of methodology and reporting is essential for improving reproducibility.

Conclusions:

• Standardization of data acquisition and analysis is vital for reliable and interoperable radiomic research.
• Multi-institutional collaboration is necessary to establish and implement effective standards.
• Addressing standardization challenges will facilitate the clinical integration of radiomic tools.