European radiology
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To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets. ⋯ • The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management.
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Multicenter Study
A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study.
Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. ⋯ • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.
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Multicenter Study
Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.
To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. ⋯ • Input matrix size significantly affected the performance of a deep learning-based CAD for detection of nodules or masses on chest radiographs. • The matrix size 896 showed the best performance in two different CNN detection models. • The optimal matrix size of chest radiographs could enhance CAD performance without additional training data.
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To develop a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer (BCa). ⋯ • DWI is superior to T2WI sequence in reflecting the heterogeneous differences between NMIBC and MIBC, and multisequence MRI helps in the preoperative prediction of muscle-invasive status of BCa. • Co-occurrence (CM), run-length matrix (RLM), and gray-level size zone matrix (GLSZM) features were the favorable feature categories for the prediction of muscle-invasive status of BCa. • The Radscore (proposed multisequence MRI-based radiomics signature) helps predict preoperatively muscle invasion. Combination with the MRI-determined tumor stalk further improves prediction.