Our objective was to compare the diagnostic performance and diagnostic confidence of convolutional neural networks (CNN) to radiologists in characterizing small hypoattenuating hepatic nodules (SHHN) in colorectal carcinoma (CRC) on CT scans. Retrosp...
BACKGROUND: Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for disease-related...
There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision suppor...
Improved drug loading content, bioavailability, and controlled release in targeted tissue have been major bottlenecks in the design of precision nanomedicine. Herein, a tumor-specific and multiple-stimuli responsive nano-riceball is proposed and vali...
IMPORTANCE: Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains.
Radiotherapy treatment planning requires accurate modeling of the delivered patient dose, including radiation scatter effects, multi-leaf collimator (MLC) leaf transmission, interleaf-leakage, etc. In fluence map optimization (FMO), a simple dose mod...
BACKGROUND AND AIMS: Detection and characterization of focal liver lesions (FLLs) is key for optimizing treatment for patients who may have a primary hepatic cancer or metastatic disease to the liver. This is the first study to develop an EUS-based c...
Journal of cancer research and clinical oncology
Aug 27, 2020
PURPOSE: Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI...
The conventional image segmentation techniques have a lot of issues with highest computational cost and low level accuracy for medical image diagnosis and genome analysis. The deep learning based optimization models utilize to predict the liver cance...
BACKGROUND: Posthepatectomy liver failure is a worrisome complication after major hepatectomy for hepatocellular carcinoma and is the leading cause of postoperative mortality. Recommendations for hepatectomy for hepatocellular carcinoma are based on ...
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