Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post–T1pre and T2–FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.
ObjectiveTo characterise the clinical course of delirium for patients with COVID-19 in the intensive care unit, including postdischarge neuropsychological outcomes.DesignRetrospective chart review and prospective survey study.SettingIntensive care units, large academic tertiary-care centre (USA).ParticipantsPatients (n=148) with COVID-19 admitted to an intensive care unit at Michigan Medicine between 1 March 2020 and 31 May 2020 were eligible for inclusion.Primary and secondary outcome measuresDelirium was the primary outcome, assessed via validated chart review method. Secondary outcomes included measures related to delirium, such as delirium duration, antipsychotic use, length of hospital and intensive care unit stay, inflammatory markers and final disposition. Neuroimaging data were also collected. Finally, a telephone survey was conducted between 1 and 2 months after discharge to determine neuropsychological function via the following tests: Family Confusion Assessment Method, Short Blessed Test, Patient-Reported Outcomes Measurement Information System Cognitive Abilities 4a and Patient-Health Questionnaire-9.ResultsDelirium was identified in 108/148 (73%) patients, with median (IQR) duration lasting 10 (4–17) days. In the delirium cohort, 50% (54/108) of patients were African American and delirious patients were more likely to be female (76/108, 70%) (absolute standardised differences >0.30). Sedation regimens, inflammation, delirium prevention protocol deviations and hypoxic-ischaemic injury were likely contributing factors, and the most common disposition for delirious patients was a skilled care facility (41/108, 38%). Among patients who were delirious during hospitalisation, 4/17 (24%) later screened positive for delirium at home based on caretaker assessment, 5/22 (23%) demonstrated signs of questionable cognitive impairment or cognitive impairment consistent with dementia and 3/25 (12%) screened positive for depression within 2 months after discharge.ConclusionPatients with COVID-19 commonly experience a prolonged course of delirium in the intensive care unit, likely with multiple contributing factors. Furthermore, neuropsychological impairment may persist after discharge.
Background: Stroke is a devastating perioperative complication without effective methods for prevention or diagnosis. The objective of this study was to analyze evidence-based strategies for detecting cerebrovascular vulnerability and injury in a high-risk cohort of non-cardiac surgery patients. Methods: This was a single-center, prospective cohort study. Fifty patients undergoing non-cardiac surgery were recruited −25 with known cerebrovascular disease and 25 matched controls. Neurologic vulnerability was measured with intraoperative cerebral oximetry as the primary outcome. Perioperative neurocognitive testing and serum biomarker analysis (S-100β, neuron specific enolase, glial fibrillary acid protein, and matrix metalloproteinase-9) were measured as secondary outcomes. Results: Cerebral desaturation events (an oximetry decrease ≥20% from baseline or <50% absolute value for ≥3 min) occurred in 7/24 (29%) cerebrovascular disease patients and 2/24 (8.3%) controls (relative risk 3.5, 95% CI 0.81–15.2; P = 0.094). Cognitive function trends were similar in both groups, though overall scores (range: 1,500–7,197) were ~1 standard deviation lower in cerebrovascular patients across the entire perioperative period (−1,049 [95% CI −1,662, −436], P < 0.001). No significant serum biomarker differences were found between groups over time. One control patient experienced intraoperative hypoxic-ischemic injury, but no robust biomarker or oximetry changes were observed. Conclusions: Cerebrovascular disease patients did not demonstrate dramatic differences in cerebral oximetry, cognitive trajectory, or molecular biomarkers compared to controls. Moreover, a catastrophic hypoxic-ischemic event was neither predicted nor detected by any strategy tested. These findings support the need for novel research into cerebrovascular risk and vulnerability.
In conjunction with conventional MRI sequences, ADC values obtained from diffusion-weighted MRI are useful to differentiate orbital infantile hemangiomas from rhabdomyosarcomas in pediatric patients.
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