BACKGROUND AND AIMS: The quality of bowel preparation is an important factor that can affect the effectiveness of a colonoscopy. Several tools, such as the Boston Bowel Preparation Scale (BBPS) and Ottawa Bowel Preparation Scale, have been developed ...
BACKGROUND: There is an ongoing debate about expanding the resection criteria for hepatocellular carcinoma (HCC) beyond the Barcelona Clinic Liver Cancer (BCLC) guidelines. We sought to determine the factors that held the most prognostic weight in th...
Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography (CT) images analysis due to its shape variation and vague boundary. The general hypothesis says that deep learning methods produce improved results on medical i...
: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients. : A...
Journal of neuroradiology = Journal de neuroradiologie
Jun 18, 2019
PURPOSE: To assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant haemangiopericytoma (HPC) from angiomatous meningioma (AM).
OBJECTIVE: To generate a nomogram based on preoperative parameters to predict the occurrence of a major complication within 30-days of robotic partial nephrectomy.
BACKGROUND: Computer navigation increases reproducibility compared to non-navigated total knee arthroplasty (TKA). Robotics navigation is a branch of computer navigation technology that might further improve accuracy of implant placement. The aim of ...
OBJECTIVES: To investigate the association between proton magnetic resonance spectroscopy (H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade.
Clinical cancer research : an official journal of the American Association for Cancer Research
Apr 11, 2019
PURPOSE: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.
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