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Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.

Temporal bone CT images obtained on a photon-counting detector CT scanner were rated as having superior spatial resolution and more critical structure visualization than those obtained on a conventional energy-integrating detector scanner, even with a substantial dose reduction.