QIFP

QIFP extracts quantitative features from planar (two-dimensional) and volumetric (three-dimensional) medical images to develop and validate imaging biomarkers for cancer biology, treatment response assessment, and clinical outcome prediction.

Key Features:

• Support for diverse image types: Processes planar (2D) and volumetric (3D) medical images across imaging modalities.
• Data integration: Accepts uploaded repositories containing linked imaging, segmentation, and clinical data and provides direct access to public datasets such as The Cancer Imaging Archive.
• Algorithm library: Provides algorithms for file conversion, image segmentation, quantitative feature extraction, and machine learning applications.
• Customization via containers: Allows users to extend functionality by uploading algorithms packaged in Docker containers.
• Clinical trial support: Provides a standardized infrastructure to enable single-center, multicenter, and virtual clinical trials for biomarker validation.

Scientific Applications:

• Imaging biomarker discovery and validation: Supports development and validation of quantitative image features as biomarkers in oncology.
• Disease progression monitoring: Enables extraction of features useful for tracking tumor progression over time.
• Treatment response assessment: Facilitates quantitative evaluation of treatment efficacy and response.
• Clinical outcome prediction: Provides features that can be used to predict patient outcomes.

Methodology:

Configurable modular pipelines that integrate image acquisition, file conversion, image segmentation, quantitative feature extraction, and machine learning to enable reproducible quantitative image analysis.