Articles: cations.
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There remains debate over the best invasive diagnostic modality for mediastinal nodal evaluation. Prior studies have limited generalizability and insufficient power to detect differences in rare adverse events. We compared the risks and costs of endobronchial ultrasound (EBUS)-guided nodal aspiration and mediastinoscopy performed for any indication in a large national cohort. ⋯ When performed as isolated procedures, EBUS is associated with lower risks and costs compared with mediastinoscopy. Future studies comparing the effectiveness of EBUS vs mediastinoscopy in the community at large will help determine which procedure is superior or if trade-offs exist.
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Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep learning approaches. It remains unclear whether we can accurately detect and locate basal cell carcinoma (BCC) using smartphone-captured images. ⋯ Based on the accessible MOIs via smartphone photography, we developed two deep learning frameworks for recognizing BCC pathology with high sensitivity and specificity. This work opens a new avenue for automatic BCC diagnosis in different clinical scenarios. What's already known about this topic? The diagnosis of basal cell carcinoma (BCC) is labour intensive due to the large number of images to be examined, especially when consecutive slide reading is needed in Mohs surgery. Deep learning approaches have demonstrated promising results on pathological image-related diagnostic tasks. Previous studies have focused on whole-slide images (WSIs) and leveraged classification on image patches for detecting and localizing breast cancer metastases. What does this study add? Instead of WSIs, microscopic ocular images (MOIs) photographed from microscope eyepieces using smartphone cameras were used to develop neural network models for recognizing BCC automatically. The MOI- and WSI-based models achieved comparable areas under the curve around 0·95. Two deep learning frameworks for recognizing BCC pathology were developed with high sensitivity and specificity. Recognizing BCC through a smartphone could be considered a future clinical choice.