Clinical trial · Observational
The PROSECCA Study, Answering New Questions in Prostate Cancer
Improving Radiotherapy in PROState Cancer Using EleCtronic Population-based healthCAre Data: The PROSECCA Study, Answering New Questions in Prostate Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Nearly half of all cancer patients receive radiotherapy as part of their treatment and although it is effective at destroying cancerous lesions deep within the body, this comes at the cost of damaging healthy, or normal, tissues. With 50% of cancer patients surviving for 10 years or more, these patients can be left with life-changing side effects from their radiotherapy. It is clear that more must be done to limit damage to normal healthy tissue without compromising annihilation of the tumour and curing patients. The key to this is personalising an individual's radiotherapy treatment, in other words rather than assuming that all tumours respond similarly to radiotherapy, the treatment is optimised for an individual. To date, approaches to do this have been restricted to small numbers of carefully selected patients, are inordinately expensive, and not suitable for rolling out into everyday practice across the NHS. There is however another way, namely using Artificial Intelligence (AI) combined with an individual's healthcare record. By linking together large numbers of healthcare records at a national level, combined with the power of AI, the PROSECCA project will transform radiotherapy and cancer care.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (3)
- measure
- PSA relapse free survival
- timeFrame
- Plus/minus 10 years from date of radiotherapy treatment
- description
- PSA relapse free survival will be measured from the date of treatment. This information will be obtained from historical patient records. The expected number of events in this category was calculated using the following approach. 5 year endpoint, 10% event rate based on Conventional or hypofractionated high dose intensity modulated radiotherapy for prostate cancer (CHHIP) trial Data up to 2015 resulting in sample size of 11,250 (75% of 15,000) Total Events = 1125
- measure
- Overall Survival
- timeFrame
- Plus/minus 10 years from date of radiotherapy treatment
- description
- Overall survival will be measured from the date of treatment. This information will be obtained from historical patient records. The expected number of events in this category was calculated using the following approach. 10 year endpoint, 29% event rate based on RT01 radiotherapy trial Data up to 2010 resulting in sample size of 7,500 (50% of 15,000) Total Events = 2175
- measure
- Radiotherapy Toxicity
Eligibility
Eligibility (as posted)
- Sex
- Male
Show eligibility criteria text
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
References
Publications (1)
- DERIVEDNailon WH, Noble DJ, Harrison E, Yang Z, Elliot S, MacNair A, Beckett G, Hallam A, Sheikh A, Mills N, Halliday R, Morrison D, Chalmers A, Cameron D, Gourley C, Hall P, Lilley C, Carruthers LJ, Trainer M, Burns D, Dee F, Andiappa S, Lonsdale A, Couper M, Farnan K, McLellan J, Miller A, Ogg J, Moses J, Colligan S, MacDonald G, McPhail N, Niblock P, MacLeod N, Davies ME, Laurenson DI, Hopgood JR, Boyle D, Paterson C, Grose D, Phillips I, Harrow S, Berger T, Shelley LEA, Sanders I, Henderson S, Duffton A, Mitchell J, Rutherford A, McLaren DB. Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data. BMJ Open. 2026 Feb 2;16(2):e104408. doi: 10.1136/bmjopen-2025-104408. PMID 41628931