The aim of this study is to improve the accuracy of survival predictions for patients with metastatic prostate cancer. Metastatic prostate cancer typically has a poor prognosis, with only 1 in 3 patients surviving past 5 years after diagnosis. Consequently, improving patient outcomes for this disease is imperative. Currently, doctors rely on clinical data like age, cancer stage, and blood test results to estimate the likelihood of survival.

This research examines if advanced image analytics from patient scans, such as tumour volume, tumour surface area, pixel intensity, and tumour uniformity within scans, can aid these predictions. Identifying such prognostic markers from both clinical data and imaging analytics could pave the way for tailored treatment plans. For instance, anticipating a shorter survival time might prompt doctors to fast-track treatment or consider alternative approaches to improve patient outcomes. Ultimately, this study strives to provide clinicians with tools for predicting patient outcomes and tailoring treatment plans accordingly.