Dataset Information

Implementation of multimodal computed tomography in a telestroke network: Five-year experience.

ABSTRACT: AIMS:Penumbral selection is best-evidence practice for thrombectomy in the 6-24 hour window. Moreover, it helps to identify the best responders to thrombolysis. Multimodal computed tomography (mCT) at the primary centre-including noncontrast CT, CT perfusion, and CT angiography-may enhance reperfusion therapy decision-making. We developed a network with five spoke primary stroke sites and assessed safety, feasibility, and influence of mCT in rural hospitals on decision-making for thrombolysis. METHODS:Consecutive patients assessed via telemedicine from April 2013 to June 2018. Clinical outcomes were measured, and decision-making compared using theoretical models for reperfusion therapy applied without mCT guidance. Symptomatic intracranial hemorrhage (sICH) was assessed according to Safe Implementation of Treatments in Stroke Thrombolysis Registry criteria. RESULTS:A total of 334 patients were assessed, 240 received mCT, 58 were thrombolysed (24.2%). The mean age of thrombolysed patients was 70 years, median baseline National Institutes of Health Stroke Scale was 10 (IQR 7-18) and 23 (39.7%) had a large vessel occlusion. 1.7% had sICH and 3.5% parenchymal hematoma. Three months poststroke, 55% were independent, compared with 70% in the non-thrombolysed group. CONCLUSION:Implementation of CTP in rural centers was feasible and led to high thrombolysis rates with low rates of sICH.

SUBMITTER: Garcia-Esperon C

PROVIDER: S-EPMC7052799 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Implementation of multimodal computed tomography in a telestroke network: Five-year experience.

Garcia-Esperon Carlos C Soderhjelm Dinkelspiel Frode F Miteff Ferdi F Gangadharan Shyam S Wellings Tom T O Brien Bill B Evans James J Lillicrap Tom T Demeestere Jelle J Bivard Andrew A Parsons Mark M Levi Chris C Spratt Neil James NJ

CNS neuroscience & therapeutics 20190930 3

<h4>Aims</h4>Penumbral selection is best-evidence practice for thrombectomy in the 6-24 hour window. Moreover, it helps to identify the best responders to thrombolysis. Multimodal computed tomography (mCT) at the primary centre-including noncontrast CT, CT perfusion, and CT angiography-may enhance reperfusion therapy decision-making. We developed a network with five spoke primary stroke sites and assessed safety, feasibility, and influence of mCT in rural hospitals on decision-making for thrombo ...[more]

PMID: 31568661

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Project description:BACKGROUND:Telestroke provides access to vascular neurology expertise for hospitals lacking stroke coverage, and its use has risen rapidly in the past decade. We aim to characterize consultations, spoke behavior, and the relationship between spoke telestroke utilization (number of telestroke consults per year) and spoke alteplase treatment metrics in an academic telestroke network. METHODS AND RESULTS:We analyzed prospectively collected data on all telestroke consults from 2003 to 2018. Trends in network performance and spoke characteristics were analyzed using generalized estimating equations and Kendall ?? nonparametric tests as appropriate. Unadjusted and adjusted linear regression models determined associations between telestroke utilization and treatment metrics. The network included 2 hubs and 43 spokes with 12 803 consults performed during the study period. Network growth overall was +1.8 spokes per year, and median duration of spoke participation was 7.9 years. The numbers of consults and alteplase-treated patients increased annually, even after adjusting for the number of spokes in the network (P<0.01 for both). Although times from last seen well to spoke emergency department arrival and to consult request increased, door-to-needle time, time from teleconsult request to callback, and time from teleconsult to alteplase administration all decreased (all P<0.01). With time, the network included more spokes without a Primary Stroke Center designation. In adjusted analyses, for every 10 telestroke consults requested by a spoke, the spoke door-to-needle decreased by 1.8 minutes (P=0.02), number of patients treated with alteplase was an additional 1.7 (P<0.01), and the percent of eligible patients treated with alteplase increased by 8% (P=0.03). CONCLUSIONS:Telestroke network size and utilization increased over time. Increased use of teleconsults was associated with increased and timely use of alteplase. Over time, the delivery of timely emergency care has improved significantly among emergency departments participating in this telestroke network. Replication of these findings in other networks is warranted.

Project description:PurposeOur purpose was to report outcomes of a novel palliative radiation therapy protocol that omits computed tomography simulation and prospectively collects electronic patient-reported outcomes (ePROs).Methods and materialsPatients receiving extracranial, nonstereotactic, linear accelerator-based palliative radiation therapy who met inclusion criteria (no mask-based immobilization and a diagnostic computed tomography within 4 weeks) were eligible. Global pain was scored with the 11-point numerical pain rating scale (NPRS). Patients were coded as having osseous or soft tissue metastases and no/mild versus severe baseline pain (NPRS ≥ 5). Pain response at 4 weeks was measured according to the international consensus (no analgesia adjustment). Transition to ePRO questionnaires was completed in 3 phases. Initially, pain assessments were collected on paper for 11 months, then pilot ePROs for 1 month and then, after adjustments, revised ePROs from 1 year onwards. ePRO feasibility criteria were established with reference to the paper-based process and published evidence.ResultsBetween May 2018 and November 2019, 542 consecutive patients were screened, of whom 163 were eligible (30%), and 160 patients were successfully treated. The proportion of patients eligible for the study improved from approximately 20% to 50% by study end. Routine care pain monitoring via ePROs was feasible. One hundred twenty-seven patients had a baseline NPRS recording. Ninety-five patients had osseous (61% severe pain) and 32 had soft tissue (25% severe pain) metastases. Eighty-four patients (66%) were assessable for pain response at 4 weeks. In the 41 patients with severe osseous pain, overall and complete pain response was 78% and 22%, respectively.ConclusionsBy study completion, 50% of patients receiving palliative extracranial radiation therapy avoided simulation, streamlining the treatment process and maximizing patient convenience. Pain response for patients with severe pain from osseous lesions was equivalent to published evidence.

Project description:BackgroundMultimodal analysis has shown great potential in the diagnosis and management of cancer. This study aimed to determine the multimodal data associations between radiological, pathologic, and molecular characteristics in bladder cancer.MethodsA retrospective study of computed tomography (CT), pathologic slice, and RNA sequencing data from 127 consecutive adult patients in China who underwent bladder surgery and were pathologically diagnosed with bladder cancer was conducted. A total of 200 radiological and 1,029 pathologic features were extracted by radiomics and pathomics. Multimodal associations analysis and structural equation modeling were used to measure the cross-modal associations and structural relationships between CT and pathologic slice. A convolutional neural network was constructed for molecular subtyping based on multimodal imaging features. Class activation maps were used to examine the feature contribution in model decision-making. Cox regression and Kaplan-Meier survival analysis were used to explore the relevance of multimodal features to the prognosis of patients with bladder cancer.ResultsA total of 77 densely associated blocks of feature pairs were identified between CT and whole slide images. The largest cross-modal associated block reflected the tumor-grade properties. A significant relation was found between pathological features and molecular subtypes (β=0.396; P<0.001). High-grade bladder cancer showed heterogeneity of significance across different scales and higher disorders at the microscopic level. The fused radiological and pathologic features achieved higher accuracy (area under the curve: 0.89; 95% CI: 0.75-1.0) than the unimodal method. Thirteen prognosis-related features from CT and whole slide images were identified.ConclusionsOur work demonstrated the associations between CT, pathologic slices, and molecular signatures, and the potential to use multimodal data analysis in related clinical applications. Multimodal data analysis showed the potential of cross-inference of modal data and had higher diagnostic accuracy than the unimodal method.

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Project description:BackgroundMultimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning.PurposeIn this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images.MethodsThe hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis.ResultsIn fivefold cross-validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods.ConclusionThe proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.

Dataset Information

Implementation of multimodal computed tomography in a telestroke network: Five-year experience.

Publications

Implementation of multimodal computed tomography in a telestroke network: Five-year experience.

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