- Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
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.
- Prediction of Wound Failure in Patients with Head and Neck Cancer Treated with Free Flap Reconstruction: Utility of CT Perfusion and MR Perfusion in the Early Postoperative Period
CT perfusion and dynamic contrast-enhanced MR imaging are both promising imaging techniques to predict wound complications after head and neck free flap reconstruction.
- A New Frontier in Temporal Bone Imaging: Photon-Counting Detector CT Demonstrates Superior Visualization of Critical Anatomic Structures at Reduced Radiation Dose
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.
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