OnceraPlus
A smiling woman outdoors under a clear blue sky

Understand your
personal risk before
immunotherapy

OnceraPlus helps identify how your unique clinical and biological factors may impact your treatment results.

What OnceraPlus gives you

Information that is personal to you

Go beyond population averages and understand your potential risk based on your clinical information.

No additional tests

Get insights using information your care team already has, without another blood draw, biopsy or hospital visit.

Clearer next steps

See which of the details you entered raised or lowered your estimate, and use it to have a more informed conversation with your doctor.

Why trust Oncera Plus?

Built for this, from
the ground up

Oncera Plus was built specifically to predict immunotherapy toxicity risk, trained and validated on clinical outcomes. It is MHRA registered.

Trained on
patient data

The model is built on clinical data from 3,000+ patients, including 500+ with lung cancer who received immunotherapy.

Working with NHS hospitals

We're currently working with a number of NHS hospitals as we expand the data behind our models.

Research evidence

Our approach to predicting immunotherapy response & toxicity has been presented at leading oncology conferences and published as conference abstracts.

Journal of Clinical Oncology (2023 ASCO Annual Meeting):

a machine learning algorithm to predict immunotherapy response in small cell and non-small cell lung cancer

Annals of Oncology (ESMO 2023):

companion abstract on the same lung cancer response model

Journal of Clinical Oncology (2024 ASCO Annual Meeting):

immunotherapy toxicity prediction in melanoma using machine learning

Clinical Cancer Research (AACR 2025):

a precision-medicine approach to melanoma immunotherapy, predicting response, adverse events, and hospital admissions using explainable AI

NHS England, Queen Mary University of London, European Union, Brunel University London and KEEP+

How It Works

Current model: Toxicity prediction in lung cancer. Prediction models based on other diseases will be available soon.

01

Create Account

Register or login securely to access your workspace.

02

Input Clinical Data

Enter patient-specific clinical factors and biomarkers.

03

Get Results

Receive comprehensive risk assessment and predictions instantly.

Common questions

What exactly is OnceraPlus?

OnceraPlus is a clinical decision-support platform designed to give patients and clinicians personalised insights about cancer treatment.

Our current model estimates the risk of immunotherapy-related side effects in people with lung cancer. It considers several pieces of routinely collected clinical information and produces a report showing the estimated risk and the factors that influenced it. We are developing additional models to support more cancer types and treatment decisions in the future.

OnceraPlus supports informed conversations with your healthcare team. It does not provide a diagnosis, choose a treatment or replace clinical judgement.

How is OnceraPlus different from ChatGPT?

OnceraPlus uses a focused clinical model built for a specific risk assessment. It does not generate general medical advice or replace your healthcare team.

How accurate is the model?

The model provides a risk estimate based on validated clinical research. No prediction is certain, so your result should always be interpreted with your clinician.

How is my result calculated?

The OnceraPlus prediction model calculates your result from the clinical information you enter. Your report also shows which of those details raised or lowered the estimate, and by how much.

What does "toxicity" mean?

Here, toxicity means any immune-related side effect of any grade recorded in a patient's clinical notes during immunotherapy treatment. The estimate does not predict which specific side effect may occur or how severe it may be.

What should I do with my result?

Take the report to your healthcare team and use it to support a conversation about your personal circumstances and treatment plan.