Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,053
datasets available to search
ShareScore release 0.9.0
Dataset results
1,053 results for “Computed Tomography”
Dataset related to article "Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma"
<p>PURPOSE:</p> <p>To appraise the ability of a radiomics signature to predict clinical outcome after stereotactic body radiation therapy (SBRT) for pancreas carcinoma.</p> <p>METHODS:</p> <p>A cohort of 100 patients was included in this retrospective, single institution analysis. Radiomics texture features were extracted from computed tomography (CT) images obtained for the clinical target volume. The cohort of patients was randomly divided into two separate groups for the training (60 patients) and validation (40 patients). Cox regression models were built to predict overall survival and local control. The significant predictors at univariate analysis were included in a multivariate model. The quality of the models was appraised by means of area under the curve and concordance index.</p> <p>RESULTS:</p> <p>A clinical-radiomic signature associated with Overall Survival (OS) was found significant in both training and validation sets (p = 0.01 and 0.05 and concordance index 0.73 and 0.75 respectively). Similarly, a signature was found for Local Control (LC) with p = 0.007 and 0.004 and concordance index 0.69 and 0.75. In the low risk group, the median OS and LC in the validation group were 14.4 and 28.6 months while in the high-risk group were 9.0 and 17.5 months respectively.</p> <p>CONCLUSION:</p> <p>A CT based radiomic signature was identified which correlate with OS and LC after SBRT and allowed to identify low and high-risk groups of patients.</p>
Dataset related to article "Cost-effectiveness of second-line diagnostic investigations in patients included in the DANTE trial: a randomized controlled trial of lung cancer screening with low-dose computed tomography."
<p>AIM:</p> <p>The aim of this study was to analyze the economic efficiency of second-line diagnostic investigations in patients with undetermined lung nodules.</p> <p>PARTICIPANTS AND METHODS:</p> <p>A retrospective review of all surgical cases included in the DANTE trial from 2001 to 2006 for lung cancer screening was performed. Overall, 217 patients and 261 lung nodules were analyzed. The cohort was divided into patients investigated with PET and/or computed tomography (CT)-guided biopsy (PET-CTB protocol; N=100), compared with those assessed with serial low-dose CT scans (standard protocol; N=161). Outpatient's and inpatient's costs were expressed in euros and derived from the Italian National Health Service. Ineffective costs were defined as the cost of procedures that lead to avoidable surgical intervention.</p> <p>RESULTS:</p> <p>The diagnostic accuracy of the two protocols was 91% for the standard (sensitivity 100%, specificity 91%, positive predictive value 26%, and negative predictive value 100%) and 90% for the PET-CTB protocol (sensitivity 98%, specificity 81%, positive predictive value 85%, and negative predictive value 97%). Average costs for outpatient's diagnostics were 694 and 1.462 euros, respectively, for the standard and PET-CTB protocol. Average inpatient's costs for both protocols were 12.121 euros. The two protocols showed comparable effectiveness in terms of outpatient's costs (94 and 90%, respectively; P=0.252). Inpatient's costs were effective in 36% of cases monitored according to the standard protocol compared with 85% of patients investigated with PET-CTB protocol. Ineffective costs corresponded to 64 and 15%, respectively (P<0.0001).</p> <p>CONCLUSION:</p> <p>Despite a higher average cost for outpatient's diagnostics, the implementation of PET imaging with or without CT-guided needle biopsy in the workup of suspicious lung nodules results in reduced unnecessary harm and costs related to inpatient's procedures.</p>
A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography
<p>Full dataset of segmentations for both the thoracic artery and the proximal pulmonary arteries (format: standard NIfTI, nii) from Saitta S, Sturla F, Caimi A, Riva A, Palumbo MC, Nano G, Votta E, Corte AD, Glauber M, Chiappino D, Marrocco-Trischitta MM, Redaelli A. A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography. J Digit Imaging. 2022 Jan 26. doi: 10.1007/s10278-021-00535-1. Epub ahead of print. PMID: 35083618.</p> <p>Abstract</p> <p>Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.</p> <p> </p>
Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features
<p>Dataset from Schiaffino S, Codari M, Cozzi A, Albano D, Alì M, Arioli R, Avola E, Bnà C, Cariati M, Carriero S, Cressoni M, Danna PSC, Della Pepa G, Di Leo G, Dolci F, Falaschi Z, Flor N, Foà RA, Gitto S, Leati G, Magni V, Malavazos AE, Mauri G, Messina C, Monfardini L, Paschè A, Pesapane F, Sconfienza LM, Secchi F, Segalini E, Spinazzola A, Tombini V, Tresoldi S, Vanzulli A, Vicentin I, Zagaria D, Fleischmann D, Sardanelli F. Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features. J Pers Med. 2021 Jun 3;11(6):501. doi: 10.3390/jpm11060501. PMID: 34204911; PMCID: PMC8230339.</p> <p>Abstract</p> <p>Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney <em>U</em> test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, <em>p</em> < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, <em>p</em> < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.</p>
Dataset related to the article "Computed tomography predictors of structural valve degeneration in patients undergoing transcatheter aortic valve implantation with balloon-expandable prostheses"
<p>This record contains raw data related to the article "Computed tomography predictors of structural valve degeneration in patients undergoing transcatheter aortic valve implantation with balloon-expandable prostheses"</p> <p><strong>Objectives: </strong>Computed tomography (CT) provides excellent anatomy assessment of the aortic annulus (AoA) and is utilized for pre-procedural planning of transcatheter aortic valve implantation (TAVI). We sought to investigate if geometrical characteristics of the AoA determined by CT may represent predictors of structural valve degeneration (SVD) in patients undergoing TAVI with balloon-expandable valves.</p> <p><strong>Methods: </strong>Retrospective study on 124 consecutive patients (mean age:79±7 years; female: 61%) undergoing balloon-expandable TAVI prospectively enrolled in a registry<strong>. </strong>AoA maximum diameter (D<sub>max</sub>), minimum diameter (D<sub>min</sub>), and area were assessed using pre-procedural CT. SVD was identified during follow-up with transthoracic echocardiography documenting structural prosthetic valve abnormalities with or without hemodynamic changes. </p> <p><strong>Results: </strong>The mean follow up was 5.9±1.7 years. SVD was found in 48 out of 124 patients (38%). AoA D<sub>max</sub>, D<sub>min</sub> and area were significantly smaller in patients with SVD compared to patients without SVD (25.6±2.2 mm vs. 27.1±2.8 mm , p=0.012; 20.5±2.1 mm vs. 21.8±2.1 mm ,p=0.001 and 419±77 mm<sup>2 </sup>vs. 467±88 mm<sup>2</sup>,p=0.002, respectively). At univariable analysis, female sex, BSA, 23-mm prosthetic valve size, D<sub>max</sub> <27.1 mm and a D<sub>min</sub> < 19.9 mm were associated with SVD whereas at multivariable analysis, only D<sub>min</sub> <19.9 mm (OR=2.873, 95% CI: 1.191-6.929,p=0.019) and female sex (OR=2.659, 95% CI: 1.095-6.458, p=0.031) were independent predictors of SVD.</p> <p><strong>Conclusions: </strong>Female sex and AoA D<sub>min</sub> < 19.9 mm are associated with SVD in patients undergoing TAVI with balloon expandable valves. When implanting large prostheses in order to avoid paraprosthetic regurgitation, caution should be observed due to the risk of excessive stretching of the AoA D<sub>min,</sub> which may play a role in SVD.</p>
A Combined Deep Learning System for Automatic Detection of "Bovine" Aortic Arch on Computed Tomography Scans
<p>A Combined Deep Learning System for Automatic Detection of “Bovine” Aortic Arch on Computed Tomography Scans</p> <p><em>Appl. Sci.</em> <strong>2022</strong>, <em>12</em>(4), 2056; <a href="https://doi.org/10.3390/app12042056">https://doi.org/10.3390/app12042056</a></p> <p>Abstract</p> <p>The “bovine” aortic arch is an anatomic variant consisting in a common origin of the innominate and left carotid artery (CILCA), associated with a greater risk of thoracic aortic diseases (aneurysms and dissections), stroke, and complications after endovascular procedures. CILCA can be detected by visual assessment of computed tomography (CT) chest scans, but it is rarely reported. We developed a deep learning (DL) segmentation-plus-classification system to automatically detect CILCA based on 302 CT studies acquired at 2 centers. One model (3D U-Net) was trained from scratch (supervised by manual segmentation), validated, and tested for the automatic segmentation of the aortic arch and supra-aortic vessels. Three DL architectures (ResNet50, DenseNet-201, and SqueezeNet), pre-trained over millions of common images, were trained, validated, and tested for the automatic classification of CILCA versus non-CILCA, supervised by radiologist’s classification. The 3D U-Net-plus-DenseNet-201 was found to be the best system (Dice index 0.912); its classification performance obtained from internal, independent testing on 126 patients gave a receiver operating characteristic area under the curve of 87.0%, sensitivity 66.7%, specificity 90.5%, positive predictive value 87.5%, negative predictive value 73.1%, positive likelihood ratio 7.0, and negative likelihood ratio 0.4. In conclusion, a combined DL system applied to chest CT scans was developed and proven to be an effective tool to detect individuals with “bovine” aortic arch with a low rate of false-positive findings.</p>
Dataset related to the article "Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"
<p>This record contains raw data related to the article “Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"</p> <p><strong>Purpose: </strong>To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation.</p> <p><strong>Methods: </strong>One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DL<sub>rest</sub>) and stress dataset (CTP-DL<sub>stress</sub>) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DL<sub>rest</sub>, and CCTA + CTP-DL<sub>stress</sub> was measured and compared. The time of analysis for CTP stress, CTP-DL<sub>rest</sub>, and CTP-DL<sub>Stress</sub> was recorded.</p> <p><strong>Results: </strong>Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTP<sub>Stress</sub> were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DL<sub>rest</sub> and CCTA + DL<sub>stress</sub> were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DL<sub>Rest</sub>, and CCTA + CTP-DL<sub>Stress</sub> significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001).</p> <p><strong>Conclusion: </strong>Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress<sub>.</sub>.</p>
Lung Ultrasound in Patients With SARS-COV-2 Pneumonia Correlations With Chest Computed Tomography, Respiratory Impairment, and Inflammatory Cascade
<p>Objectives—Lung ultrasound (LUS) might be comparable to chest computed tomography (CT) in detecting parenchymal and pleural pathology, and in monitoring interstitial lung disease. We aimed to describe LUS characteristics of patients during the hospitalization for COVID-19 pneumonia, and to compare the extent of lung involvement at LUS and chest-CT with inflammatory response and the severity of respiration impairment.<br> Methods—During a 2-week period, we performed LUS and chest CT in hospitalized patients affected by COVID-19 pneumonia. Dosages of high sensitivity C-reactive protein (HS-CRP), D-dimer, and interleukin-6 (IL-6) were also obtained. The index of lung function (P/F ratio) was calculated from the blood gas test. LUS and CT scoring were assessed using previously validated scores. <br> Results—Twenty-six consecutive patients (3 women) underwent LUS 34 - 14 days from the early symptoms. Among them, 21 underwent CT on the same day of LUS. A fair association was found between LUS and CT scores (R = 0.45, P = .049), which became stronger if the B-lines score on LUS was not considered (R = 0.57, P = .024). LUS B-lines score correlated with IL-6 levels (R = 0.75, P = 0.011), and the number of involved lung segments detected by LUS correlated with the P/F ratio (R = 0.60, P = .019) but not with HS-CRP and D-Dimer levels. No correlations were found between CT scores and inflammations markers or P/F.<br> Conclusion—In patients with COVID-19 pneumonia, LUS was correlated with both the extent of the inflammatory response and the P/F ratio. </p>
A Normative Dataset of Healthy Pediatric Cranial Computed Tomography (CT) Images
This dataset contains cranial CT images from 100 healthy pediatric subjects, aged one month to ten years, to address the limited availability of public normative reference data for healthy pediatric cranial CT imaging. Supporting data includes subject demographics (age, sex, race/ethnicity) and a Python script for ease of data loading. It is designed to support research reproducibility by providing a common baseline for developing and validating AI algorithms, conducting normative studies of neurodevelopment, and serving as a control cohort for pediatric neurological research.
Risk factors for myocardial injury and death in patients with COVID-19: insights from a cohort study with chest computed tomography
<p>Clinical study about risk factors of myocardial injury and death in COVID-19</p> <p><a href="https://doi.org/10.1093/cvr/cvaa193">https://doi.org/10.1093/cvr/cvaa193</a></p> <p> </p>
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</p>
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data Set from the study Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</p>
3D Reconstruction of a Calcified Aortic Valve Cusp Based on Micro-Computed Tomography.
<p>Video showing 3D reconstruction of a calcified aortic valve cusp based on micro-computed tomography.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.