European journal of radiology
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Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the presence of a micropapillary or a solid component is identified as an independent predictor of prognosis, suggesting a more extensive resection. The purpose of our study is to explore imaging phenotyping using a method combining radiomics with deep learning (RDL) to predict high-grade patterns within lung ADC. ⋯ High-grade lung ADC based on histologic pattern spectrum in GGO lesions might be predicted by the framework combining radiomics with deep learning, which reveals advantage over radiomics alone.
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Background parenchymal enhancement (BPE) often affects interpretation of dynamic contrast-enhanced (DCE) MRI. There is limited evidence that reduced BPE is a feature of ultrafast DCE (UF-DCE) MRI. We aimed to evaluate the effect of BPE levels on lesion detectability on UF-DCE MRI in comparison with conventional DCE MRI. ⋯ Images with lower BPE can be achieved using UF-DCE MRI and may be advantageous when assessing breast lesions among patients with higher BPE or premenopausal women.
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To establish and validate a radiomics nomogram for predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa). ⋯ The radiomics nomogram, which incorporates the multiparametric MRI-based radiomics signature and clinical risk factors, can be conveniently used to promote individualized prediction of BM in patients with newly diagnosed PCa.
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Multicenter Study
Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.
To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images. ⋯ Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.
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Multicenter Study
Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule.
To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs). ⋯ The radiomics nomogram we developed can preoperatively distinguish between LAC and TBG in patient with a SPSN.