Citations

Background Coronavirus disease 2019 (COVID-19) is associated with a prothrombotic state; leading to multiple sequelae. We sought to detect whether thromboelastography (TEG) parameters would be able to detect thromboembolic events in patients hospitalized with COVID-19. Methods We performed a retrospective multicenter case–control study of the Collaborative Research to Understand the Sequelae of Harm in COVID (CRUSH COVID) registry of 8 tertiary care level hospitals in the United States (US). This registry contains adult patients with COVID-19 hospitalized between March 2020 and September 2020. Results A total of 277 hospitalized COVID-19 patients were analyzed to determine whether conventional coagulation TEG parameters were associated with venous thromboembolic (VTE) and thrombotic events during hospitalization. A clotting index (CI) >3 was present in 45.8% of the population, consistent with a hypercoagulable state. Eighty-three percent of the patients had clot lysis at 30 min (LY30) = 0, consistent with fibrinolysis shutdown, with a median of 0.1%. We did not find TEG parameters (LY30 area under the receiver operating characteristic [ROC] curve [AUC] = 0.55, 95% CI: 0.44-0.65, P value = .32; alpha angle [α] AUC = 0.58, 95% CI: 0.47-0.69, P value = .17; K time AUC = 0.58, 95% CI: 0.46-0.69, P value = .67; maximum amplitude (MA) AUC = 0.54, 95% CI: 0.44-0.64, P value = .47; reaction time [R time] AUC = 0.53, 95% CI: 0.42-0.65, P value = .70) to be a good discriminator for VTE. We also did not find TEG parameters (LY30 AUC = 0.51, 95% CI: 0.42-0.60, P value = .84; R time AUC = 0.57, 95%CI: 0.48-0.67, P value .07; α AUC = 0.59, 95%CI: 0.51-0.68, P value = .02; K time AUC = 0.62, 95% CI: 0.53-0.70, P value = .07; MA AUC = 0.65, 95% CI: 0.57-0.74, P value < .01) to be a good discriminator for thrombotic events. Conclusions In this retrospective multicenter cohort study, TEG in COVID-19 hospitalized patients may indicate a hypercoagulable state, however, its use in detecting VTE or thrombotic events is limited in this population.

Real-time intraoperative delineation of cancer and non-cancer brain tissues, especially in the eloquent cortex, is critical for thorough cancer resection, lengthening survival, and improving quality of life. Prior studies have established that thresholding optical attenuation values reveals cancer regions with high sensitivity and specificity. However, threshold of a single value disregards local information important to making more robust predictions. Hence, we propose deep convolutional neural networks (CNNs) trained on labeled OCT images and co-occurrence matrix features extracted from these images to synergize attenuation characteristics and texture features. Specifically, we adapt a deep ensemble model trained on 5,831 examples in a training dataset of 7 patients. We obtain 93.31% sensitivity and 97.04% specificity on a holdout set of 4 patients without the need for beam profile normalization using a reference phantom. The segmentation maps produced by parsing the OCT volume and tiling the outputs of our model are in excellent agreement with attenuation mapping-based methods. Our new approach for this important application has considerable implications for clinical translation.

AbstractObjectivesThe objective of this article is to review options for regional pedicled reconstruction for large head and neck defects in a salvage setting.MethodsRelevant regional pedicled flaps were identified and reviewed. Expert opinion and supporting literature were used to summarize and describe the available options.ResultsSpecific regional pedicled flap options are presented including the pectoralis major flap, deltopectoral flap, supraclavicular flap, submental flap, latissimus flap, and trapezius flap.ConclusionsRegional pedicled flaps are useful options in a salvage setting even for large defects and should be in the armamentarium of any reconstructive head and neck surgeon. Each flap option carries specific characteristics and considerations.

AbstractBackgroundDeep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN‐based denoising models is the procurement of training data consisting of paired examples of high‐noise and low‐noise CT images. Obtaining these paired data are not practical in a clinical setting where the raw projection data is not available. This work outlines a technique to optimize CNN denoising models using methods that are available in a routine clinical setting.PurposeTo demonstrate a phantom‐based training framework for CNN noise reduction that can be efficiently implemented on any CT scanner.MethodsThe phantom‐based training framework uses supervised learning in which training data are synthesized using an image‐based noise insertion technique. Ten patient image series were used for training and validation (9:1) and noise‐only images obtained from anthropomorphic phantom scans. Phantom noise‐only images were superimposed on patient images to imitate low‐dose CT images for use in training. A modified U‐Net architecture was used with mean‐squared‐error and feature reconstruction loss. The training framework was tested for clinically indicated whole‐body‐low‐dose CT images, as well as routine abdomen‐pelvis exams for which projection data was unavailable. Performance was assessed based on root‐mean‐square error, structural similarity, line profiles, and visual assessment.ResultsWhen the CNN was tested on five reserved quarter‐dose whole‐body‐low‐dose CT images, noise was reduced by 75%, root‐mean‐square‐error reduced by 34%, and structural similarity increased by 60%. Visual analysis and line profiles indicated that the method significantly reduced noise while maintaining spatial resolution of anatomic features.ConclusionThe proposed phantom‐based training framework demonstrated strong noise reduction while preserving spatial detail. Because this method is based within the image domain, it can be easily implemented without access to projection data.