AIMC Topic: Subcutaneous Fat

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Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model.

Cancer biomarkers : section A of Disease markers Jan 1, 2024
OBJECTIVES: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images.
Humans Neural Networks, Computer Tomography, X-Ray Computed Female Breast Neoplasms Deep Learning Subcutaneous Fat
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Fully Automated Segmentation of Connective Tissue Compartments for CT-Based Body Composition Analysis: A Deep Learning Approach.

Investigative radiology Jun 1, 2020
OBJECTIVE: Body composition comprises prognostic information in patients with various malignancies and can be opportunistically determined from routine computed tomography (CT) scans. However, accurate assessment of patients with alterations, for exa...
Humans Neural Networks, Computer Retrospective Studies Tomography, X-Ray Computed Female Male Deep Learning Body Composition Subcutaneous Fat
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Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography.

Korean journal of radiology Jan 1, 2020
OBJECTIVE: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images.
Humans Tomography, X-Ray Computed Female Aged Aged, 80 and over Young Adult Adult Male Middle Aged Deep Learning Adolescent Image Processing, Computer-Assisted Muscle, Skeletal Image Enhancement Intra-Abdominal Fat Subcutaneous Fat
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