CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma
CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma
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DOI：
10.1148/radiol.2016151455
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发表时间：
2016-07-01
期刊：
影响因子：
19.7
通讯作者：
Gillies, Robert J.
中科院分区：
文献类型：
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作者：
Liu, Ying;Kim, Jongphil;Gillies, Robert J.
目的：回顾性研究一组亚洲患者手术切除肺腺癌中表皮生长因子受体（EGFR）突变状态、主要组织学亚型和计算机断层扫描（CT）特征之间的关系。材料和方法：本研究由机构审查委员会批准，并放弃知情同意。回顾性分析385例手术切除的肺腺癌术前胸部CT表现。总共评估了30个CT描述符。EGFR外显子18-21突变用扩增难解突变系统测定。多重逻辑回归分析确定了EGFR突变状态的独立因素。采用反向消去法选择最终模型，并与DeLong、DeLong和Clarke-Pearson的非参数方法比较受试者工作特征曲线（ROC）下的两个区域。结果：385例患者中有168例（43.6%）发生EGFR突变。突变在(a)女性患者中更为常见（P < 0.001）；(b)从不吸烟者（P < 0.001）；(c)鳞状显性腺癌（P = .001）或中等病理分级（P < .001）；(e)较小的肿瘤（P < 0.001）；(f)肿瘤伴毛刺（P = 0.019）、磨玻璃样混浊（GGO）或混合GGO （P < 0.001）、支气管充气征（P = 0.006）、泡状透光（P < 0.001）、血管会聚（P = 0.024）、邻近支气管维管束增厚（P = 0.027）或胸膜缩回（P < 0.001）；(g)没有胸膜附着的肿瘤（P = 0.004）、边界明确的肿瘤（P = 0.010）、明显的非均匀强化（P = 0.001）、严重的外周肺气肿（P = 0.002）、严重的外周纤维化（P = 0.013）或淋巴结病（P = 0.028）。对于具有临床变量和CT特征的模型来说，隐藏egfr激活突变的最重要和最显著的独立预后因素是那些从未吸烟的人，以及那些肿瘤较小、泡状透光、均匀增强或胸膜收缩的人，当调整组织学亚型、病理分级或相邻支气管血管束增厚时。ROC曲线分析显示，临床变量联合CT特征（ROC曲线下面积= 0.778）优于单独使用临床变量（ROC曲线下面积= 0.690）。结论：肺腺癌的CT影像特征与临床变量相结合，比单独使用临床变量更能预测EGFR突变状态。(c) rsna, 2016
Purpose: To retrospectively identify the relationship between epidermal growth factor receptor (EGFR) mutation status, predominant histologic subtype, and computed tomographic (CT) characteristics in surgically resected lung adenocarcinomas in a cohort of Asian patients.materials and Methods: This study was approved by the institutional review board, with waiver of informed consent. Preoperative chest CT findings were retrospectively evaluated in 385 surgically resected lung adenocarcinomas. A total of 30 CT descriptors were assessed. EGFR mutations at exons 18-21 were determined by using the amplification refractory mutation system. Multiple logistic regression analyses were performed to identify independent factors of harboring EGFR mutation status. The final model was selected by using the backward elimination method, and two areas under the receiver operating characteristic curve (ROC) were compared with the nonparametric approach of DeLong, DeLong, and Clarke-Pearson.Results: EGFR mutations were found in 168 (43.6%) of 385 patients. Mutations were found more frequently in (a) female patients (P < .001); (b) those who had never smoked (P < .001); (c) those with lepidic predominant adenocarcinomas (P = .001) or intermediate pathologic grade (P < .001); (e) smaller tumors (P < .001); (f) tumors with spiculation (P = .019), ground- glass opacity (GGO) or mixed GGO (P < .001), air bronchogram (P = .006), bubblelike lucency (P < .001), vascular convergence (P = .024), thickened adjacent bronchovascular bundles (P = .027), or pleural retraction (P < .001); and (g) tumors without pleural attachment (P = .004), a well- defined margin (P = .010), marked heterogeneous enhancement (P = .001), severe peripheral emphysema (P = .002), severe peripheral fibrosis (P = .013), or lymphadenopathy (P = .028). The most important and significantly independent prognostic factors of harboring EGFR-activating mutation for the model with both clinical variables and CT features were those who had never smoked and those with smaller tumors, bubblelike lucency, homogeneous enhancement, or pleural retraction when adjusting for histologic subtype, pathologic grade, or thickened adjacent bronchovascular bundles. ROC curve analysis showed that use of clinical variables combined with CT features (area under the ROC curve = 0.778) was superior to use of clinical variables alone (area under the ROC curve = 0.690).Conclusion: CT imaging features of lung adenocarcinomas in combination with clinical variables can be used to prognosticate EGFR mutation status better than use of clinical variables alone. (C) RSNA, 2016