Overview of prognostic factors in adult gliomas Akanksha Sharma, Jerome J. Graber

BACKGROUND AND PURPOSE: MR imaging–based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present 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 patient9s own histologic data. MATERIALS AND METHODS: We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS: Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS: Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Objective To test the hypothesis that US neurologists were experiencing significant challenges with lack of personal protective equipment (PPE), rapid changes in practice, and varying institutional protocols, we conducted this survey study. The current coronavirus disease of 2019 (COVID-19) pandemic has caused widespread disease and death. Rapid increases in patient volumes have exposed weaknesses in health care systems and challenged our ability to provide optimal patient care and adequate safety measures to health care workers (HCWs). Methods A 36-item survey was distributed to neurologists around the United States through various media platforms. Results Over a 1-week period, 567 responses were received. Of these, 56% practiced in academia. A total of 87% had access to PPE, with 45% being asked to reuse PPE due to shortages. The pandemic caused rapid changes in practice, most notably a shift toward providing care by teleneurology, although a third experienced challenges in transitioning to this model. Wide variations were noted both in testing and in the guidance provided for the exposed, sick, or vulnerable HCWs. Notably, 59% of respondents felt that their practices were doing what they could, although 56% did not feel safe taking care of patients. Conclusions Results from our survey demonstrate significant variability in preparedness and responsiveness to the COVID-19 pandemic in neurology, affected by region, health care setting, and practice model. Practice guidelines from professional societies and other national entities are needed to improve protection for physicians and their patients, promote recommended practice changes during a pandemic, and optimize future preparedness for public health emergencies.