Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increased their s...
PURPOSE: This study aimed to investigate the value of integrating computed tomography (CT)-based tumor radiomics features with clinical parameters for preoperative prediction of microvascular invasion (MVI) in clear cell renal cell carcinoma (ccRCC).
PURPOSE: To evaluate the efficacy of deep learning reconstruction (DLR) in diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (SSEPI) for endometrial cancer, compared to multiplexed sensitivity-encoding (MUSE) DWI.
BACKGROUND: Lymphovascular invasion (LVI) is a significant histopathological marker associated with poor prognosis in patients. However, there is a notable lack of reliable, non-invasive preoperative tools to predict LVI accurately.
OBJECTIVES: The complex structure of tube lumen instruments (TLIs) makes them more difficult to clean compared to solid instruments. This study aims to improve the cleaning quality inspection of reusable TLIs, ensuring patient safety and clinical rel...
RATIONALE AND OBJECTIVES: The research aims to examine how CT-derived habitat radiomics can be used to predict lymphovascular invasion (LVI) in patients with T1-stage lung adenocarcinoma (LUAD), and compare its effectiveness to traditional radiomics ...
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. Th...
OBJECTIVES: To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous system lymphoma (PCNSL) and to investigate their correlation with patient prognosis.
Computer methods and programs in biomedicine
Apr 18, 2025
BACKGROUND AND OBJECTIVE: In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in colorectal cancer and its metastasis. Again, exceeding genomic arrangements of AKT2 can be held responsib...
Capturing rich multi-scale features is essential to address complex variations in medical image segmentation. Multiple hybrid networks have been developed to integrate the complementary benefits of convolutional neural networks (CNN) and Transformers...
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