the prosecca study, answering new questions in prostate cancer

Conditions: Prostate Cancer

Summary: 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.

as an example of how this technology is beginning to emerge, proof of concept data from the team has shown that using ai it is possible to identify patients at increased risk of damage from radiotherapy long before they receive any radiation as part of their treatment. however, to move these ai-based approaches from the research domain into the clinic requires significant effort, which is the aim of prosecca study. the project will use ai to analyse healthcare records from up to 15,000 prostate cancer patients who underwent radiotherapy in scotland. through linkage of data obtained specifically for radiotherapy and data held within each patient's unique healthcare history it will be possible to establish new relationships between a patient's medical history and how well a patient responds to radiotherapy. by establishing what factors or information in a patient's complex healthcare record indicate that an individual may have a poor response to treatment, or an increased risk of side effects from radiation, it will be possible to identify these patients earlier than is currently possible and adapt treatment accordingly. furthermore, identifying these important factors would improve radiotherapy treatment for prostate cancer patients in the future.

Eligibility 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

Keywords: artificial intelligence radiotherapy reactions clinical decisions predict outcomes patient individualised response machine learning ai personalized radiotherapy radiotherapy adaptation