Deep learning models for whole-body positron emission tomography/computed tomography (PET/CT) hold significant clinical potential for disease detection, treatment planning, and response assessment. However, the time and cost associated with expert annotations remain major bottlenecks in developing robust and generaliza...
Amartya Bhattacharya, B. A. Biswas, B. Wagle et al.· 0 citations
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
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
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
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