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Clinical trial Recruiting at 1 site Observational Men only

PROSECCA: Using AI and health records to predict how people with prostate cancer respond to radiotherapy

Official title: Improving Radiotherapy in PROState Cancer Using EleCtronic Population-based healthCAre Data: The PROSECCA Study, Answering New Questions in Prostate Cancer

Run by University of Edinburgh · NCT06714630 · Data updated 2026-11-12

This project uses AI on past medical and radiotherapy records of men treated for prostate cancer to try to predict who will have poor treatment responses or long-term side effects.

More Prostate Cancer trials

Quick facts

Study typeObservational
SexMen only
Enrollment15000 (estimated)
SponsorUniversity of Edinburgh
Academic / other
Drugs / interventionsradiation
Locations1 site (Edinburgh, Lothian)
StatusRecruiting
StartedJuly 1, 2024
Main results expectedSeptember 30, 2027
Study endsSeptember 30, 2028
Registry record updatedAugust 26, 2026
Trial IDNCT06714630 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Ideal candidates for the dataset are men with prostate cancer who received full-course external beam radiotherapy with available radiotherapy planning CT, treatment planning data, regular PSA follow-up, and linked healthcare records.

Probably not a fit

Patients without complete imaging, treatment planning data, PSA follow-up, or linked healthcare records, or those who did not complete radiotherapy or lack long-term follow-up, are unlikely to be included or benefit.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

PROSECCA links radiotherapy planning data, diagnostic and treatment CT images, PSA follow-up, and broader healthcare records from up to 15,000 prostate cancer patients treated in Scotland. The team will apply machine learning to these linked datasets to find patterns and factors in a patient’s medical history that predict radiotherapy outcomes and toxicity. The work builds on local proof-of-concept data showing AI can identify higher-risk patients before treatment. The goal is to turn those predictive signals into tools that could be used in routine care to personalise radiotherapy decisions.

Why it matters

Potential benefit: If successful, the approach could identify patients at higher risk of poor response or radiation side effects earlier so clinicians can tailor or avoid treatments that cause long-term harm.

How similar studies have gone: This approach builds on early proof-of-concept work from the team and on emerging AI studies in radiotherapy, but such methods are not yet widely adopted into routine clinical practice.

Who can join: the full rules

Show full inclusion / exclusion criteria 
Inclusion Criteria:

* External beam radiotherapy delivered by a linear accelerator
* Prostate Specific Antigen (PSA) recorded at regular intervals after radiotherapy
* Minimum of 10 year survival post-radiotherapy
* Diagnostic Computerised Tomography (CT) acquired
* Radiotherapy planning CT acquired
* Radiotherapy treatment planning data available
* Corresponding healthcare data available to infer toxicity events (ref previous work by Lemanska et al)

Exclusion Criteria:

* Incomplete course of radiotherapy
* No PSA data
* No follow-up corresponding healthcare data available
* No imaging data available

Where it's running

  • University of Edinburgh
    Edinburgh, Lothian, United Kingdom     Recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
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Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.