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Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2016

[GSI Quantitative Parameters: Preoperative Diagnosis of Metastasis Lymph Nodes in Lung Cancer].

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Yang Fengfeng, Dong Jie, Yan Xiaolong, Wang Xiuting, Fu Xiaojiao, Zhang Tong

Аннотация

BACKGROUND: Mediastinal involvement in lung cancer is an important prognostic factor affecting survival, and accurate staging of the mediastinum lymph node correctly identifies patients who can benefit the most from surgery. The aim of this study is to investigate the value of dual-energy spectral computed tomography (DEsCT) imaging in differentiating metastatic from non-metastatic lymph nodes in lung cancer.

METHODS: Forty-eight patients with non-small cell lung cancer (NSCLC) underwent arterial (AP) and portal venous (PP) phase contrast-enhanced DEsCT imaging followed by surgical treatment. gemstone spectral imaging (GSI) data images were reconstructed and transmitted to an offline workstation. GSI quantitative parameters, including lymph-node size, CT value, IC, water concentration, and spectral curve. Differences were tested for statistical significance using the two-sample t test. ROC analysis was performed to assess diagnostic performance.

RESULTS: The mean short-axis diameter of metastatic LNs, slope of the spectral Hounsfield unit curve (λHU), normalized iodine concentration measured during, and both AP and PP were significantly higher in metastatic lymph node than that in benign lymph nodes. The best parameter for detecting metastatic lymph nodes was AP λHU when a threshold λHU of 2.75 was used; sensitivity, specificity, and accuracy were 88.2%, 88.4%, and 87.0%, respectively.

CONCLUSIONS: Quantitative assessment with gemstone spectral imaging quantitative parameters showed higher accuracy than the qualitative assessment of conventional CT imaging features for the preoperative diagnosis of metastatic lymph nodes in patients with lung cancer.
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Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты