Coronavirus Narratives: Drawing Lessons for Effective Crisis Management

Some critics claim that President Trump has downplayed the risks of coronavirus at the onset of the pandemic, describing it merely as flu at a time when other administrative officials in the CDC and HHS were warning of a very grave future with the virus and implored quick action. The Narrative Policy Framework (NPF) helps to systematically measure the difference between policy narratives and track their changes over time. This research utilizes the NPF to explore whether there was a disconnect between narratives posed by President Trump and agency officials in Coronavirus Task Force at the onset of the pandemic and considers the implications. Content analyses of Coronavirus Task Force press briefings from 31 January 2020 to 17 April 2020 were performed. There were significant differences in the frequency, timing, and early introduction of some characters and policy solutions. There was also some overlap in many occasions, highlighting the wholistic nature of the pandemic response that fosters complementary rather than necessarily similar policy action

Hierarchical prediction of registration misalignment using a convolutional LSTM: application to chest CT scans

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: "correct" 0-3 mm, "poor" 3-6 mm and "wrong" over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where the prediction is gradually refined from coarse to fine. Our solution is based on a convolutional Long Short-Term Memory (LSTM), using hierarchical misalignment predictions on three resolutions of the image pair, leveraging the intrinsic strengths of an LSTM for this problem. The convolutional LSTM is trained on a set of artificially generated image pairs obtained from artificial displacement vector fields (DVFs). Results on chest CT scans show that incorporating multi-resolution information, and the hierarchical use via an LSTM for this, leads to overall better F1 scores, with fewer misclassifications in a well-tuned registration setup. The final system yields an accuracy of 87.1%, and an average F1 score of 66.4% aggregated in two independent chest CT scan studies.Radiolog

On-Treatment Platelet Reactivity is a Predictor of Adverse Events in Peripheral Artery Disease Patients Undergoing Percutaneous Angioplasty

Objectives: Few data are available on the association between a different entity of platelet inhibition on antiplatelet treatment and clinical outcomes in patients with peripheral artery disease (PAD). The aim of this study was to evaluate the degree of on-treatment platelet reactivity, and its association with ischaemic and haemorrhagic adverse events at follow up in PAD patients undergoing percutaneous transluminal angioplasty (PTA). Methods: In this observational, prospective, single centre study, 177 consecutive patients with PAD undergoing PTA were enrolled, and treated with dual antiplatelet therapy with aspirin and a P2Y12 inhibitor. Platelet function was assessed on blood samples obtained within 24 h from PTA by light transmission aggregometry (LTA) using arachidonic acid (AA) and adenosine diphosphate (ADP) as agonists of platelet aggregation. High on-treatment platelet reactivity (HPR) was defined by LTA ≥ 20% if induced by AA, and LTA ≥ 70% if induced by ADP. Follow up was performed to record outcomes (death, major amputation, target vessel re-intervention, acute myocardial infarction and/or myocardial revascularisation, stroke/TIA, and bleeding). Results: HPR by AA and HPR by ADP were found in 45% and 32% of patients, respectively. During follow up (median duration 23 months) 23 deaths (13%) were recorded; 27 patients (17.5%) underwent target limb revascularisation (TLR), two (1.3%) amputation, and six (3.9%) myocardial revascularisation. Twenty-four patients (15.6%) experienced minor bleeding. On multivariable analysis, HPR by AA and HPR by ADP were independent predictors of death [HR 3.8 (1.2–11.7), p =.023 and HR 4.8 (1.6–14.5), p =.006, respectively]. The median value of LTA by ADP was significantly lower in patients with bleeding complications than in those without [26.5% (22–39.2) vs. 62% (44.5–74), p <.001). LTA by ADP ≤ 41% was independently associated with bleeding HR 14.6 (2.6–24.0), p =.001] on multivariable analysis. Conclusions: In this study a high prevalence of on-clopidogrel and aspirin high platelet reactivity was found, which was significantly associated with the risk of death. Conversely, a low on-clopidogrel platelet reactivity was associated with a higher risk of bleeding. These results document that the entity of platelet inhibition is associated with both thrombotic and bleeding complications in PAD patients

Joint registration and segmentation via multi-task learning for adaptive radiotherapy of prostate cancer

Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information. We propose to merge these tasks not only on the loss level, but on the architectural level as well. We studied this approach in the context of adaptive image-guided radiotherapy for prostate cancer, where planning and follow-up CT images as well as their corresponding contours are available for training. At testing time the contours of the follow-up scans are not available, which is a common scenario in adaptive radiotherapy. The study involves two datasets from different manufacturers and institutes. The first dataset was divided into training (12 patients) and validation (6 patients), and was used to optimize and validate the methodology, while the second dataset (14 patients) was used as an independent test set. We carried out an extensive quantitative comparison between the quality of the automatically generated contours from different network architectures as well as loss weighting methods. Moreover, we evaluated the quality of the generated deformation vector field (DVF). We show that MTL algorithms outperform their Single-Task Learning (STL) counterparts and achieve better generalization on the independent test set. The best algorithm achieved a mean surface distance of 1.06 +/- 0.3 mm, 1.27 +/- 0.4 mm, 0.91 +/- 0.4 mm, and 1.76 +/- 0.8 mm on the validation set for the prostate, seminal vesicles, bladder, and rectum, respectively. The high accuracy of the proposed method combined with the fast inference speed, makes it a promising method for automatic re-contouring of follow-up scans for adaptive radiotherapy, potentially reducing treatment related complications and therefore improving patients quality-of-life after treatment. The source code is available at https://github.com/moelmahdy/JRS-MTL.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Esophageal tumor segmentation in CT images using a Dilated Dense Attention Unet (DDAUnet)

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with air pockets, feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79 +/- 0.20, a mean surface distance of 5.4 +/- 20.2mm and 95% Hausdorff distance of 14.7 +/- 25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via https://github.com/yousefis/DenseUnet_Esophagus_Segmentation.Biological, physical and clinical aspects of cancer treatment with ionising radiatio