REVERT - taRgeted thErapy for adVanced colorEctal canceR paTients

Exploring the key findings of REVERT’s articles: the final brochure

The REVERT project’s groundbreaking research culminates in a brochure featuring four impactful articles, now available to the public. This brochure easy-to-read, distributed during the project’s final event held in Rome last 10th December 2024, highlights the REVERT innovative approaches to personalized medicine and AI-driven solutions in oncology.

1. Development and Validation of AI Models

The article provides an in-depth look at the advanced AI algorithms created during the project. These models are designed to predict treatment efficacy for patients with unresectable metastatic colorectal cancer (mCRC). A strong emphasis is placed on explainable AI, which enhances trust and usability for clinicians.

2. Clinical Trial Insights

The second article focuses on a clinical study involving six oncology centers across Europe. The results demonstrate the real-world application of REVERT’s AI-based decision support system (DSS) in assisting oncologists to select optimal treatments for mCRC patients.

3. Data Integration Frameworks

Highlighting the consolidation of diverse datasets, this article explores the development of the REVERT database. It details how clinical, biobank, and real-world data were harmonized to create a robust foundation for predictive analytics and personalized treatment planning.

4. In Vitro Studies and Future Perspectives

The final article delves into innovative in vitro studies utilizing organoids. These studies have helped refine predictive models and open avenues for novel therapeutic combinations, paving the way for future advancements in targeted cancer therapies.

A Legacy of Innovation

This brochure underscores the REVERT project’s dedication to integrating cutting-edge technology and collaborative expertise. It serves as a first approach to better understand the advancing personalized medicine and AI-driven healthcare solutions.

FINAL BROCHURE

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 848098”.

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