Alex Graudenzi, PhD
Cancer evolution and computational biology.
Publication recordResearch & development
Turning cancer history into clinically useful foresight.
EvoClin develops time-aware AI models from longitudinal clinical and molecular data to study treatment response, relapse and molecular evolution. Traceable inputs, clinician oversight and validation guide this work. The platform is not yet clinically validated or available for clinical use.
Where we are going
A research program built around the next clinical decision.
Data infrastructure is the starting point for developing and evaluating patient-level prediction.
- 01
Represent disease over time
A source-linked patient model across reports, therapies and sequencing.
- 02
Model what may happen next
Interpretable algorithms for response, relapse and future molecular states.
- 03
Validate before deployment
Planned pathway: retrospective benchmarking, site calibration and prospective evaluation.
Source-traceableClinician-in-the-loopValidation before deployment
Scientific foundation
World-class expertise in cancer evolution.
The 2026 NEJM Evidence paper is the latest result of an extensive, continuing body of peer-reviewed work. This foundation now feeds EvoClin's proprietary R&D pipeline. Clinical validation of the platform remains a separate development milestone.
NEJM EvidenceVolume 5, Issue 6 · 2026DOI 10.1056/EVIDoa2500124
Latest publication
Prognostic Score for Myelodysplastic Syndromes Based on Molecular Evolution.
Daniele Ramazzotti, PhD
Bioinformatics and cancer genomics.
Publication recordIvan Civettini, MD
Clinical hematology and translational research.
Publication recordResearch and validation