Managing, storing and analyzing large-scale imaging and multi-omics datasets through QP-Discovery® platform

Solutions for data management and processing across several clinical trials.
Application Oncology

The challenge

A large RWE company was looking for a platform to centrally manage, store and quantitatively analyze imaging data across multiple cancer indications.

  • The solution should incorporate multi-omics data indexing/storage interoperability.
  • Their goal was to augment their analytical capabilities and to extract new predictive models or biomarkers by linking imaging data with electronic health records (EHR) data processed using natural language processing (NLP) techniques.

The solution

Quibim provided QP-Discovery® platform, to manage index, store and quantitatively analyze multi-omics data. The solution is built on cloud-based technology and is interoperable with other registries in a federated manner. It includes patient de-identification as well as capabilities for pseudonymization or full anonymization of imaging data, complying with HIPAA/GDPR. Quibim is also an ISO 27001-certified company.

QP-Discovery® platform is highly customizable based on the user’s requirements. It includes an embedded zero-footprint DICOM viewer for radiological readings, treatment response criteria evaluation, AI-driven organ/lesion segmentation, and automatic quantification of imaging biomarkers.

The partner used QP-Discovery® platform to find patient subtypes and extract insights from imaging data in the form of surrogate biomarkers to clinical endpoints (treatment response, progression free survival, and overall survival, among others).

The outcome

The RWE partner decided to grow the number of projects that they currently run on QP-Discovery® at a rate of 5x Year-Over-Year. The new platform provided by Quibim allows to position their brand with an AI/Radiomics offering in front of biopharma customers.

Quibim provides a private, safe, single-tenant and continuously available platform to the partner while AI experts from both teams collaborate in the development of novel imaging biomarker panels.