Author

Michael S. Leapman

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Aug 2026

Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer.

OBJECTIVE To evaluate the performance and interpretability of multiple ML algorithms for survival prediction in prostate cancer (CaP) and to assess whether these approaches can improve upon established clinical risk prediction models. METHODS Using the Surveillance, Epidemiology, and End Results (SEER) 17 Database, w...

Isaac E. Kim, Cecile P. G. Meier-Scherling, S. Zhou et al. · Urologic oncology · 0 citations

Review Jul 2026

Active surveillance for intermediate-risk prostate cancer: a systematic review and pooled analysis.

Growing evidence suggests that AS appears oncologically safe in well-selected patients, and magnetic resonance imaging will play a key role in the appropriate selection and follow-up of patients.

Alessandro Uleri, L. Cella, T. Long-Depaquit et al. · BJU International · 0 citations

Review Aug 2026

Mirror Worlds: The Shared Regulatory Architecture of Cell Fate in Development and Cancer.

How emerging single-cell and spatial multi-omics technologies, integrated with artificial intelligence-based modeling, enable mapping of cell state landscapes and transitional trajectories, transforming lineage plasticity from a descriptive phenomenon into a measurable and predictable property of tumor evolution is dis...

Melanie Fraidenburg, Long-Jun Li, R. Kwon et al. · Cancer Letters · 0 citations