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相关概念视频

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Imaging Studies IV: Magnetic Resonance Imaging01:27

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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相关实验视频

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In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
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可解释的基于CT的多相放射性分析,用于在手术前区分良性和恶性固体瘤:一个多中心研究.

Yaohai Wu1, Fei Cao1, Hanqi Lei1

  • 1Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.

Abdominal radiology (New York)
|May 11, 2024
PubMed
概括

使用对比度增强CT (CECT) 的机器学习模型可以有效地区分良性瘤和恶性瘤. 排泄阶段 (EP) 模型在区分瘤类型方面表现最好.

关键词:
计算机断层扫描 (CT) 是一种计算机断层扫描.无线电学 (Radiomics) 是一种辐射学.随机的森林随机的森林脏瘤:脏瘤是指脏的瘤.莎普利的添加式解释

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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 区分良性瘤和恶性瘤对于适当的患者管理至关重要.
  • 增强对比度的CT (CECT) 是瘤特征的关键成像方式.

研究的目的:

  • 开发和比较机器学习模型,使用三相CECT区分良性和恶性瘤.
  • 评估基于单个和组合CECT阶段的模型的性能.

主要方法:

  • 放射性特征从CECT.的皮质甲状腺 (CP),脏 (NP) 和分泌 (EP) 阶段提取出来.
  • 随机森林 (RF) 模型使用单相和全相 (TP) 特性进行训练.
  • 模型被内部和外部验证,SHapley添加式解释 (SHAP) 用于解释.

主要成果:

  • 射频模型在训练和验证组中都实现了高AUC.
  • 排泄阶段 (EP) 模型在训练组中显示出最高的AUC (0.930),在验证组中表现强 (0.921).
  • "原始_形状_平度"特征被确定为EP模型预测中最重要的特征.

结论:

  • 基于三相CECT的机器学习模型对于分辨脏瘤是有效的.
  • 基于EP特征的射频模型在良性与恶性瘤分类方面表现出卓越的性能.