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The authors evaluated tumor cell density using a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. They collected 82 image-recorded biopsy samples, from 18 patients with primary GBM. With multivariate modeling, transfer learning improved performance (r = 0.88) compared with one-model-fits-all (r = 0.39). They conclude that transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Endovascular thrombectomy has proven efficacy for a wide range of patients with large-vessel occlusion stroke and in selected cases up to 24 hours from onset. While primary stroke centers have increased the proportion of patients withstroke receiving thrombolytic therapy, delays can be encountereduntil patients with LVO are identified and transferred from the primary stroke center to acomprehensive stroke center. Therefore, any extra steps need to be carefullyweighed. The use of CTA (especially multiphase) at the primary stroke center levelhas many advantages in expediting the transfer of appropriate patients to a comprehensive center.