Daniel Spratt, MD, outlines the limitations of PSA-, T stage-, and Gleason-based risk stratification and explains how AI-derived, continuous risk estimates could address them.
Daniel Spratt, MD, explains what the STAR-CAP cohort is, what an MMAI risk estimate represents, and why calibration benchmarking differs from clinical validation.
Daniel Spratt, MD, reviews how closely MMAI-predicted 10-year distant metastasis and prostate cancer–specific mortality tracked observed STAR-CAP outcomes, including among patients treated with surgery.
Daniel Spratt, MD, explains how he layers MMAI results onto NCCN risk groups and how he manages discordant results in both directions.
Daniel Spratt, MD, outlines the validation standards urologists should expect before adopting AI-based tools, where AI-driven digital pathology is headed, and his key takeaway from the benchmarking data.
