Journal: Journal of Neurointerventional Surgery

Article Title: Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

doi: 10.1136/jnis-2022-019970

Figure Lengend Snippet: Workflow of the clinical test, including data collection. Flowchart shows patient selection with criteria and workflow to the clinical test for Heuron ASPECTS evaluation.

Article Snippet: In the reference study, the agreement between automated ASPECTS software, such as e-ASPECTS, Frontier ASPECTS (prototype v2, Siemens, Germany), and RAPID ASPECTS, was compared with the expert consensus as a reference standard, and it was concluded that reasonable performance was confirmed for all three programs.

Techniques: Selection

Journal: Journal of Neurointerventional Surgery

Article Title: Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

doi: 10.1136/jnis-2022-019970

Figure Lengend Snippet: The automatic ASPECTS estimation based on the deep-learning model and its performance. (A) progress in estimating the aspects, and (B) output of Heuron ASPECTS. The study results are shown as a Bland–Altman plot (C) of experts’ consensus and Heuron ASPECTS. The mean difference is 0.03, and the upper and lower limits of agreement are 2.80 and −2.74, respectively, satisfying prespecified primary outcomes. (D) The intraclass correlation coefficient (ICC) is 0.78 (95% CI: 0.73 to 0.83), showing good to excellent agreement.

Article Snippet: In the reference study, the agreement between automated ASPECTS software, such as e-ASPECTS, Frontier ASPECTS (prototype v2, Siemens, Germany), and RAPID ASPECTS, was compared with the expert consensus as a reference standard, and it was concluded that reasonable performance was confirmed for all three programs.

Techniques:

Journal: Journal of Neurointerventional Surgery

Article Title: Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

doi: 10.1136/jnis-2022-019970

Figure Lengend Snippet: Sensitivity, specificity, and AUC of Heuron ASPECTS prediction in comparison with experts’ consensus in each region of interest and dichotomized cut-off points

Article Snippet: In the reference study, the agreement between automated ASPECTS software, such as e-ASPECTS, Frontier ASPECTS (prototype v2, Siemens, Germany), and RAPID ASPECTS, was compared with the expert consensus as a reference standard, and it was concluded that reasonable performance was confirmed for all three programs.

Techniques: Comparison

Journal: Frontiers in Oncology

Article Title: Development and Validation of a Radiomics Nomogram Based on 18 F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

doi: 10.3389/fonc.2020.01042

Figure Lengend Snippet: The workflow in developing a radiomic overall survival nomogram. Computed tomography (CT) and positron emission tomography (PET) images were segmented semiautomatically using the Siemens radiomics prototype. Features including histogram features, shape features, texture features, and wavelet features were extracted from CT and PET images using the software. Three rad-scores were calculated using the least absolute shrinkage and selection operator Cox regression based on subsets of CT, PET, and PET/CT radiomic features. The predictive ability of CT, PET, and PET/CT rad-scores on overall patient survival was evaluated. The overall survival nomogram was constructed based on the PET/CT rad-score and clinicopathological factors.

Article Snippet: A volume of interest(VOI)was drawn semiautomatically around the tumor by a chest radiologist (Y.B., 9 years of experience) in the lung diagnosis using the radiomics prototype (Radiomics, Frontier, Siemens) and confirmed by another chest radiologist (W.Q.G., 5 years of experience).

Techniques: Computed Tomography, Positron Emission Tomography, Software, Selection, Positron Emission Tomography-Computed Tomography, Construct

Journal: Frontiers in Oncology

Article Title: Development and Validation of a Radiomics Nomogram Based on 18 F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

doi: 10.3389/fonc.2020.01042

Figure Lengend Snippet: Harrell's concordance index of different modalities.

Article Snippet: A volume of interest(VOI)was drawn semiautomatically around the tumor by a chest radiologist (Y.B., 9 years of experience) in the lung diagnosis using the radiomics prototype (Radiomics, Frontier, Siemens) and confirmed by another chest radiologist (W.Q.G., 5 years of experience).

Techniques: Biomarker Discovery

Journal: Frontiers in Oncology

Article Title: Development and Validation of a Radiomics Nomogram Based on 18 F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

doi: 10.3389/fonc.2020.01042

Figure Lengend Snippet: Decision curve analysis for each model. The y-axis denotes the net benefit, which was calculated using true-positive and false-positive results. The radiomics nomogram model has the highest net benefit at the threshold from 0.1 to 0.9 among all positive predictions (line labeled “All”), all negative prediction (line labeled “None”), and another 4 clinical models (line labeled “Tumor volume, tumor-node-metastasis [TNM] stage, TNM stage and tumor volume, and clinical model”).

Article Snippet: A volume of interest(VOI)was drawn semiautomatically around the tumor by a chest radiologist (Y.B., 9 years of experience) in the lung diagnosis using the radiomics prototype (Radiomics, Frontier, Siemens) and confirmed by another chest radiologist (W.Q.G., 5 years of experience).

Techniques: Labeling

Journal: Journal of Thoracic Imaging

Article Title: Computed Tomography Assessment of Coronary Atherosclerosis

doi: 10.1097/RTI.0000000000000711

Figure Lengend Snippet: Selected CTA Automated Quantitative Software Utilizing HU-based AI Algorithms

Article Snippet: Syngo through Frontier Coronary Plaque Analysis , Siemens , Volumetric quantification and differentiation of lipid, fibrous, and calcified plaques , N/A.

Techniques: Software, Diagnostic Assay, Imaging