Investigating the utility of machine learning methods to predict prognosis and guide treatment decisions for people with lung cancer (Lung-ORACLE)

Data-sharing agreement from NHS England Data Uses Register. Evidence status: source reported.

Organisation: UNIVERSITY COLLEGE LONDON (UCL)
Published: 2026-09-27
Source reference: DARS-NIC-678273-F2S0V-v2.2
Jurisdiction: England
Licence: Open Government Licence v3.0, subject to NHS England terms and exclusions

Register reference: DARS-NIC-678273-F2S0V-v2.2
Applicant type: Academic
Data controllers: UNIVERSITY COLLEGE LONDON (UCL)
Agreement period: 2026-09-27 to 2027-10-20
For commercial purposes (as marked in the register): No
Release files listed by NHS England: 0

Data approved

NDRS Cancer Registrations
NDRS Linked Cancer Waiting Times (Treatments only)
NDRS Linked HES APC
NDRS Lung Cancer Data Audit (LUCADA)
NDRS National Lung Cancer Audit (NLCA)
NDRS National Radiotherapy Dataset (RTDS)
NDRS Somatic Molecular Dataset
NDRS Systemic Anti-Cancer Therapy Dataset (SACT)

Objective, as published

University College London (UCL) requires access to NHS England data for the purpose of the following research project: Investigating the utility of machine learning methods to predict prognosis and guide treatment decisions for people with lung cancer (Lung-ORACLE) The following is a summary of the aims of the research project: Derive and validate multiple prognostic models for lung cancer patients, using a variety of statistical approaches to identify the most accurate modelling approach, with a view to improve treatment decisions for lung cancer patients The following National Disease Registration Service (NDRS) National Cancer Registration and Analysis Service (NCRAS) datasets will be accessed: • NDRS Cancer Registrations (inc. Route to Diagnosis Information)- necessary because one of the main objectives of this research is to develop an accurate prognostic tool for patients diagnosed with lung cancer, to aid treatment decision making. The study team therefore require identification of people diagnosed with non-small cell lung cancer (NSCLC); details of their diagnosis date to estimate survival time; details of their cancer to include as prognostic variables (e.g. stage of cancer, grade of cancer); patient demographics which will impact survival to include as prognostic variables (e.g. age at diagnosis, ethnicity, IMD); route to diagnosis as a prognostic variable (as emergency admissions are likely to present in latter stages compared to GP referrals); cause of death and…

Expected benefits, as published

The findings of this research study are expected to contribute to evidence-based decision-making support for policy-makers, local decision-makers such as doctors, and patients, in the following ways: 1) By working collaboratively with clinicians and patients throughout the study team will ensure the research is interpreted and presented appropriately so as to increase confidence in prognostic models for cancer 2) The work will inform how such prognostic models (including lung and other cancer types) should be designed for use in clinical practice in the future. 3) The study team will review how clinician behaviour in therapeutic decision-making may change due to the availability of model predictions, and the prognostic accuracy afforded by the models versus clinician judgement alone. This…

Benefits reported by the applicant

So far the data has been used to develop risk prediction models for patients diagnosed with lung cancer to be used to aid treatment decision making in clinical practice for these patients. The next step is to validate the models by subgroup (e.g. by ethnicity) to ensure performance will maintain fairness for patients and not broader inequalities

This is the applicant's own account; OpenHDI has not verified it.

Source: https://digital.nhs.uk/services/data-access-request-service-dars/data-uses-register. Captured 2026-11-05.

Limitations: Data-sharing agreement as published in NHS England's Data Uses Register. An agreement permits access; actual data releases are listed separately. Expected and yielded benefits are as reported by the applicant and have not been independently verified. Long text fields can be cut off in NHS England's spreadsheet export at Excel's 32,767-character cell limit.