Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and...

Area of Science:

• Oncology
• Radiation Oncology
• Medical Physics
• Health Informatics

Background:

• Prostate cancer is a leading cause of cancer death in UK males, with radiotherapy being a common treatment.
• Despite radiotherapy advancements, significant side effects remain common, impacting long-term quality of life for survivors.
• Predicting individual patient response to radiotherapy is crucial for optimizing treatment outcomes and minimizing toxicity.

Purpose of the Study:

• To identify predictive biomarkers for radiotherapy response in prostate cancer using electronic healthcare data.
• To develop a tool for individualizing radiotherapy dose and treatment plans to improve cure rates and reduce side effects.
• To leverage population-based healthcare data for precision medicine in prostate cancer radiotherapy.

Main Methods:

• The PROSECCA study analyzes healthcare records of up to 15,000 prostate cancer patients treated with radiotherapy in Scotland (2010-2022).
• Data linkage includes radiotherapy specifics, electronic health records (EHRs), and digital imaging.
• Artificial intelligence and machine learning (AI/ML) will be employed to identify factors associated with poor treatment response or increased side effect risk.

Main Results:

• The study aims to identify novel predictive biomarkers for radiation response.
• The findings will enable earlier identification of at-risk patients.
• The project will provide a clinical tool for personalized radiotherapy treatment planning.

Conclusions:

• The PROSECCA study will advance personalized medicine in prostate cancer radiotherapy.
• By analyzing comprehensive electronic health records, the study seeks to improve treatment efficacy and patient outcomes.
• This research is vital for minimizing long-term radiation-induced toxicity in prostate cancer survivors.