How can prediction models and AI improve prostate cancer diagnostics and treatments?
Both prostate cancer diagnostics and treatment suffer from inefficient use of information for clinical decision making, leading to high rates of overdiagnosis and overtreatment of indolent disease at the same time as prostate cancer is the leading cause of cancer death among men. We will in our presentation discuss how we are systematically trying to develop and clinically implement prediction models and artificial intelligence (AI) systems to address these inefficiencies. We will for example discuss the combination of the Stockholm3 test with MRI to improve prostate cancer diagnostics (Eklund et al. NEJM, 2021; Nordström et al. Lancet Oncology, 2021), and show results from the development of an AI-system for diagnosis and grading of prostate cancer in biopsies (Ström et al. Lancet Oncology, 2020; Bulten et al. Nature Medicine, 2022; Olsson et al. Nature Communications 2022), which we have demonstrated can performs on par with internationally leading uro-pathologist. We will also discuss the link between AI and clinical trials and how clinical trials can be transformed into continuous learning systems, which we will exemplify with the ongoing ProBio trial for improving treatment for men with metastatic prostate cancer (Crippa et al. Trials, 2020; De Laere et al. European Urology Focus, 2022).

Speaker
Martin Eklund is a professor of epidemiology at Karolinska Institutet. His current research at KI focuses on reducing breast and prostate cancer mortality and improving diagnostic and treatment selection precision, using biomarker development, prediction modelling, and clinical trials.

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