UM - Precision Medicine Toolbox

UM - Precision Medicine Toolbox performs standardized data curation, image pre-processing, radiomics feature extraction, and exploratory feature analysis to produce harmonized radiomic feature vectors for reproducible precision-medicine model development.

Key Features:

• Data curation and harmonization: Standardizes and harmonizes heterogeneous imaging datasets and associated metadata.
• Image pre-processing: Applies configurable pre-processing steps including resampling, normalization, and segmentation handling.
• Radiomics feature extraction: Generates radiomic feature vectors suitable for downstream machine-learning pipelines.
• Parameterizable pipelines: Makes preprocessing and feature-generation steps explicit and traceable to reduce methodological variability.
• Modular components: Provides modular components for harmonizing datasets and composing preprocessing and extraction workflows.
• Feature exploration utilities: Supports visualization, summarization, and quality assessment of extracted descriptors for informed feature selection and data diagnostics.
• Interoperability: Bridges gaps among existing imaging and radiomics toolchains to support reproducible workflows.

Scientific Applications:

• Quantitative imaging workflows: Supports end-to-end quantitative medical imaging studies from curation through feature extraction.
• Radiomics for precision medicine: Produces harmonized radiomic descriptors for development and evaluation of precision-medicine machine-learning models.
• Cross-site data harmonization: Enables harmonization of heterogeneous imaging datasets to mitigate site and protocol variability.
• Feature selection and diagnostics: Enables visualization, summarization, and quality assessment to inform feature selection and data diagnostics.

Methodology:

Standardized data curation, configurable image pre-processing (resampling, normalization, segmentation handling), radiomics feature extraction, parameterizable pipeline execution, and feature-exploration operations including visualization, summarization, and quality assessment to generate radiomic feature vectors for downstream machine-learning analyses.