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CT 联合多参数 MRI 术前鉴别非小细胞肺癌病理亚型。

CT Combined with Multiparameter MRI in Differentiating Pathological Subtypes of Non-Small-Cell Lung Cancer before Surgery.

机构信息

Department of CT Diagnosis, Yan'an People's Hospital, Yan'an, Shaanxi 716000, China.

Department of Anesthesiology, The Affiliated Hospital of Yan'an University, Yan'an, Shaanxi 716000, China.

出版信息

Contrast Media Mol Imaging. 2022 May 17;2022:8207301. doi: 10.1155/2022/8207301. eCollection 2022.

Abstract

OBJECTIVE

To investigate the diagnostic value of computed tomography (CT) combined with multiparametric magnetic resonance imaging (mpMRI) for preoperative differentiation of non-small-cell lung cancer (NSCLC).

METHODS

CT and MRI imaging data were collected from all patients with squamous lung cancer and adenocarcinoma admitted to our hospital from June 2019 to December 2020 (286 cases). ROC curves were plotted to evaluate the performance of CT, mpMRI, and CT combined with mpMRI to differentiate pathological subtypes of NSCLC. Univariate and multivariate regression were used to be independent predictors of pathological subtypes of NSCLC.

RESULTS

ROC curves showed that CT combined with mpMRI had the largest area under the curve, followed by mpMRI and CT successively. Univariate regression analysis showed that gender, smoking, tumor size, morphology, marginal lobulation, marginal burr, bronchial truncation sign, and vascular convergence sign were factors influencing the pathological subtype of NSCLC. Multivariate regression analysis suggested the fact that gender, tumor size, morphology, marginal lobulation, bronchial truncation, and vascular convergence sign are likely the independent predictors of NSCLC pathological subtypes.

CONCLUSIONS

CT combined with mpMRI can effectively distinguish NSCLC pathological subtypes, which is worthy of clinical application.

摘要

目的

探讨计算机断层扫描(CT)联合多参数磁共振成像(mpMRI)对非小细胞肺癌(NSCLC)术前病理类型的诊断价值。

方法

收集 2019 年 6 月至 2020 年 12 月我院收治的鳞癌和腺癌患者的 CT 和 MRI 影像学资料(286 例)。绘制 ROC 曲线评估 CT、mpMRI 及 CT 联合 mpMRI 对 NSCLC 病理类型的鉴别效能。采用单因素和多因素回归分析 NSCLC 病理类型的独立预测因素。

结果

ROC 曲线显示 CT 联合 mpMRI 曲线下面积最大,其次为 mpMRI 和 CT。单因素回归分析显示,性别、吸烟史、肿瘤大小、形态、边缘分叶、边缘毛刺、支气管截断征、血管聚集征是影响 NSCLC 病理类型的因素。多因素回归分析提示性别、肿瘤大小、形态、边缘分叶、支气管截断、血管聚集征是 NSCLC 病理类型的独立预测因素。

结论

CT 联合 mpMRI 可有效鉴别 NSCLC 病理类型,具有临床应用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f856/9129958/1398c8739b9a/CMMI2022-8207301.001.jpg

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