QuantImage

QuantImage facilitates physician-centered radiomics research by extracting quantitative features from medical images and enabling development and evaluation of machine learning prediction models using patient-specific outcome data.

Key Features:

• Radiomics feature extraction: Extracts quantitative features from medical images for use in predictive modeling.
• Cohort management: Manages patient cohorts and associated clinical and imaging data for analysis.
• Machine learning model development: Supports development and evaluation of machine learning prediction models.
• No-code model building: Enables model development and evaluation without requiring programming expertise.
• Integration of patient-specific outcome data: Incorporates patient outcome data to train and validate predictive models.
• Model validation: Provides capabilities for validating radiomics models on clinical datasets.
• Visualization: Produces visualizations of extracted features and model results for analysis.
• Open-source tooling: Built on well-established tools and open-source libraries.
• Physician-centered workflow support: Supports clinician involvement across the radiomics workflow from extraction to validation.

Scientific Applications:

• Predictive model development: Develops radiomics-based predictive models for personalized decision support.
• Translational radiomics: Facilitates deployment and evaluation of radiomics models in clinical settings.
• Outcome-driven validation: Validates models using patient-specific outcome data to assess clinical relevance.
• Personalized medicine: Contributes to personalized medicine efforts by linking imaging features to patient outcomes.

Methodology:

Radiomics feature extraction from medical images; cohort management and integration of patient-specific outcome data; machine learning model development and evaluation; model validation and visualization; implementation using well-established tools and open-source libraries.