Revista do Colegio Brasileiro de Cirurgioes
Sep 30, 2019
OBJECTIVE: to report our initial experience with pulmonary robotic segmentectomy, describing the surgical technique, the preferred positioning of portals, initial results and outcomes.
AIM: To realise the automated prediction of soft-tissue sarcoma (STS) grading and lung metastasis based on computed tomography (CT), T1-weighted (T1W) magnetic resonance imaging (MRI), and fat-suppressed T2-weighted MRI (FST2W) via the convolutional ...
Investigate 3D (spatial and temporal) convolutional neural networks (CNNs) for real-time on-the-fly magnetic resonance imaging (MRI) reconstruction. In particular, we investigated the applicability of training CNNs on a patient-by-patient basis for t...
The application of deep learning for the detection of lymph node metastases on histologic slides has attracted worldwide attention due to its potentially important role in patient treatment and prognosis. Despite this attention, false-positive predic...
BACKGROUND: Information in Electronic Health Records is largely stored as unstructured free text. Natural language processing (NLP), or Medical Language Processing (MLP) in medicine, aims at extracting structured information from free text, and is le...
AIM: To test the diagnostic performance of a deep learning-based system for the detection of clinically significant pulmonary nodules/masses on chest radiographs.
Accurate outcome prediction is crucial for precision medicine and personalized treatment of cancer. Researchers have found that multi-dimensional cancer omics studies outperform each data type (mRNA, microRNA, methylation or somatic copy number alter...
PURPOSE: To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART).
BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.
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