Bringing cancer evolution into clinical decision making.

EvoClin is a suite of algorithms built to analyse molecular data of cancer patients to predict the future evolution of the disease

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AI molecular workflow Appropriate NGS use Decision-ready reports Evolution-informed prediction

AI-assisted clinical decision making

AI-assisted molecular requests Reduce missing exams and incomplete handoffs.

Appropriate NGS selection Guide panel choice, timing and repeat testing.

Decision-ready interpretation Combine reports, guidelines and EvoClin predictive models.

Request Complete molecular workups.

Structured exam requests from patient context.

Appropriateness Better use of molecular testing.

Guideline-aware and model-informed support for NGS choices.

Interpretation Reports translated into action.

Clinical summaries connected to risk and follow-up decisions.

What EvoClin offers

A concise AI-assisted workflow from exam request to clinical decision, built for oncology and hematology teams.

Request

AI exam request assistant

Generate structured molecular, cytogenetic, chimerism, flow cytometry and clinical-data requests from patient context.

Appropriateness

Right test, right timing

Support NGS panel choice, repeat testing and ctDNA timing with patient context, guidelines and EvoClin models.

Report

Decision-ready interpretation

Translate molecular reports, MRD, flow cytometry and longitudinal results into readable clinical summaries.

Prediction

Evolution-informed foresight

Support validated risk stratification for relapse, disease-free survival, metastasis and therapy resistance.

From request to follow-up

EvoClin is designed to sit inside the hospital pathway, connecting clinician, molecular laboratory and clinical decision making.

Before the test

Patient context is used to help prepare complete requests and suggest the most appropriate molecular exams for the clinical question.

After the report

Molecular findings are connected with clinical variables, guidelines and predictive models so the output is easier to use during case review.

During follow-up

Longitudinal modules help assess when repeat NGS or ctDNA is likely to add actionable information for the patient pathway.

Backed by rigorous science

EvoClin builds on years of work in computational cancer evolution, including frameworks such as ASCETIC that reconstruct evolutionary histories across large cohorts and identify evolution-based signatures with clinical relevance.

The project is led by researchers and clinicians with expertise in cancer genomics, bioinformatics, systems oncology and clinical haematology/oncology, with a strong track record in peer-reviewed publications and collaborative consortia.

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