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Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference

机译：区分恶性肿瘤与良性肿瘤的孤立性肺结节的CT光谱分析：碘浓度空间分布差异的价值

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Objective The objective is to assess the value of spatial distribution difference in iodine concentration between malignant and benign solitary pulmonary nodules (SPNs) by analyzing multiple parameters of spectral CT.

机译：目的通过分析频谱CT的多个参数，评估恶性和良性孤立性肺结节（SPN）之间碘浓度的空间分布差异的价值。

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期刊名称 other
作者
Linyu Wu; Guoquan Cao; Liang Zhao; Kun Tang; Jie Lin; Shouliang Miao; Tingting Lin; Jieke Sun; Xiangwu Zheng;
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年(卷),期 -1(2018),-1
年度 -1
页码 4830659
总页数 9
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1. Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules? [J] . Mai-Lin Chen, Xiao-Ting Li, Yi-Yuan Wei, Thoracic cancer. . 2019,第2期

机译：可以光谱计算断层扫描成像改善恶性和良性肺部病变之间的分化，表现为孤立纯玻璃，混合覆盖玻璃和固体结节？
2. Dual-Energy CT-Based Differentiation of Benign Posttreatment Changes From Primary or Recurrent Malignancy of the Head and Neck: Comparison of Spectral Hounsfield Units at 40 and 70 keV and Iodine Concentration [J] . Yamauchi Hideomi, Buehler Mark, Goodsitt Mitchell M., AJR: American Journal of Roentgenology : Including Diagnostic Radiology, Radiation Oncology, Nuclear Medicine, Ultrasonography and Related Basic Sciences . 2016,第3期

机译：基于双能CT的良性后处理从头颈部恶性肿瘤或复发性恶性肿瘤的区分：40和70 keV的光谱Hounsfield单位与碘浓度的比较
3. Using neighborhood gray tone difference matrix texture features on dual time point PET/CT images to differentiate malignant from benign FDG-avid solitary pulmonary nodules [J] . Song Chen, Stephanie Harmon, Timothy Perk, Cancer Imaging . 2019,第1期

机译：使用邻域灰色音差矩阵纹理特征在双时间点PET / CT图像上，以区分恶性来自良性FDG-Avid孤立性肺结核
4. Preliminary Application of Neural Network in Differentiating Benign from Malignant Solitary Pulmonary Nodule on HRCT [C] . Chen Hui, Hua, W.X., . 2005

机译：神经网络在HRCT鉴别良恶性孤立性肺结节中的初步应用
5. Pulmonary nodules on CT: Machine learning for the development and assessment of image features related to malignancy status. [D] . Byrd, Ashlee Michelle. 2015

机译：CT上的肺结节：机器学习，用于开发和评估与恶性状态有关的图像特征。
6. Can Spectral CT Imaging Improve the Differentiation between Malignant and Benign Solitary Pulmonary Nodules? [O] . Ying Zhang, Jiejun Cheng, Xiaolan Hua, -1

机译：频谱CT成像可以改善恶性和良性孤立性肺结节的区别吗？
7. Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference [O] . Linyu Wu, Guoquan Cao, Liang Zhao, 2018

机译：孤立性肺结节的光谱CT分析，用于区分病变的恶性肿瘤：碘浓度空间分布差的值

Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference

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