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术前 CT 特征预测肺腺癌中 ALK 基因重排。

Preoperative CT features for prediction of ALK gene rearrangement in lung adenocarcinomas.

机构信息

Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Clinical Research Center for Cancer, Tianjin, China.

Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Clinical Research Center for Cancer, Tianjin, China.

出版信息

Clin Radiol. 2020 Jul;75(7):562.e21-562.e29. doi: 10.1016/j.crad.2020.03.026. Epub 2020 Apr 16.

Abstract

AIM

To identify preoperative features on computed tomography (CT) associated with ALK rearrangement in lung adenocarcinomas presenting as a nodule.

MATERIALS AND METHODS

This retrospective analysis included 56 patients with ALK rearrangement and 57 that were ALK-negative. All patients had surgically resected lung adenocarcinomas <3 cm. Univariate and multivariate analyses were conducted to analyse clinicopathological and CT features associated with ALK rearrangement. Receiver operating characteristic (ROC) analyses were performed to quantify the performance status of the model.

RESULTS

ALK rearrangement was associated with lymph node metastases (p=0.004), later pathological stage (p=0.005), lower lobe (p=0.019), lobulation (p=0.006), thickened adjacent bronchovascular bundles (p=0.006), homogeneous tumour (p=0.008), absence of ground-glass opacity (GGO; p<0.001), absence of air bronchogram (p=0.010), smaller relative enhancement (p=0.019), and larger short axis of the largest lymph node (p=0.012). Cavity larger than 1 cm was found in 3 ALK-positive tumours while not in ALK-negative tumours. Multivariate analysis revealed a single predictive model with an AUC of 0.794 that lobulation (OR=4.50, p=0.026), GGO (OR=0.19, p=0.003), and short axis of the largest lymph node (OR=12.49, p=0.047) were independent predictors of ALK rearrangement status.

CONCLUSIONS

This study identified a modestly predictive radiological model to identify ALK rearrangement in small lung adenocarcinomas.

摘要

目的

确定与肺腺癌结节中 ALK 重排相关的术前 CT 特征。

材料与方法

本回顾性分析纳入 56 例 ALK 重排患者和 57 例 ALK 阴性患者。所有患者均行手术切除<3cm 的肺腺癌。采用单因素和多因素分析分析与 ALK 重排相关的临床病理和 CT 特征。进行受试者工作特征(ROC)分析以量化模型的性能状态。

结果

ALK 重排与淋巴结转移(p=0.004)、较晚的病理分期(p=0.005)、下叶(p=0.019)、分叶征(p=0.006)、相邻支气管血管束增厚(p=0.006)、均匀肿瘤(p=0.008)、无磨玻璃密度影(GGO;p<0.001)、无空气支气管征(p=0.010)、相对增强较小(p=0.019)和最大淋巴结短轴较大(p=0.012)有关。在 3 例 ALK 阳性肿瘤中发现腔大于 1cm,而在 ALK 阴性肿瘤中未发现。多因素分析显示,具有 0.794 AUC 的单一预测模型,分叶征(OR=4.50,p=0.026)、GGO(OR=0.19,p=0.003)和最大淋巴结短轴(OR=12.49,p=0.047)是 ALK 重排状态的独立预测因子。

结论

本研究确定了一个适度预测性的影像学模型,可用于识别小的肺腺癌中的 ALK 重排。

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