Department of Radiology, University of Brescia, Piazzale Spedali Civili 1, 25123, Brescia, Italy.
Department of Oncology, University of Brescia, Piazzale Spedali Civili 1, 25123, Brescia, Italy.
Radiol Med. 2021 Jun;126(6):786-794. doi: 10.1007/s11547-020-01323-7. Epub 2021 Jan 29.
To develop a CT texture-based model able to predict epidermal growth factor receptor (EGFR)-mutated, anaplastic lymphoma kinase (ALK)-rearranged lung adenocarcinomas and distinguish them from wild-type tumors on pre-treatment CT scans.
Texture analysis was performed using proprietary software TexRAD (TexRAD Ltd, Cambridge, UK) on pre-treatment contrast-enhanced CT scans of 84 patients with metastatic primary lung adenocarcinoma. Textural features were quantified using the filtration-histogram approach with different spatial scale filters on a single 5-mm-thick central slice considered representative of the whole tumor. In order to deal with class imbalance regarding mutational status percentages in our population, the dataset was optimized using the synthetic minority over-sampling technique (SMOTE) and correlations with textural features were investigated using a generalized boosted regression model (GBM) with a nested cross-validation approach (performance averaged over 1000 resampling episodes).
ALK rearrangements, EGFR mutations and wild-type tumors were observed in 19, 28 and 37 patients, respectively, in the original dataset. The balanced dataset was composed of 171 observations. Among the 29 original texture variables, 17 were employed for model building. Skewness on unfiltered images and on fine texture was the most important features. EGFR-mutated tumors showed the highest skewness while ALK-rearranged tumors had the lowest values with wild-type tumors showing intermediate values. The average accuracy of the model calculated on the independent nested validation set was 81.76% (95% CI 81.45-82.06).
Texture analysis, in particular skewness values, could be promising for noninvasive characterization of lung adenocarcinoma with respect to EGFR and ALK mutations.
开发一种基于 CT 纹理的模型，能够预测表皮生长因子受体（EGFR）突变、间变性淋巴瘤激酶（ALK）重排的肺腺癌，并在治疗前 CT 扫描中区分它们与野生型肿瘤。
对 84 例转移性肺腺癌患者的治疗前增强 CT 扫描进行了 TexRAD（英国剑桥 TexRAD 有限公司）的纹理分析。使用滤波-直方图方法，对单个 5mm 厚的中心切片进行不同空间尺度的滤波，以量化纹理特征，该切片被认为代表整个肿瘤。为了处理我们人群中突变状态百分比的类别不平衡问题，使用合成少数过采样技术（SMOTE）优化数据集，并使用嵌套交叉验证方法的广义提升回归模型（GBM）研究纹理特征与突变状态之间的相关性（在 1000 次重采样过程中平均性能）。
在原始数据集中，19 例患者为 ALK 重排，28 例患者为 EGFR 突变，37 例患者为野生型肿瘤。平衡数据集由 171 个观测值组成。在 29 个原始纹理变量中，有 17 个变量用于模型构建。原始图像和精细纹理的偏度是最重要的特征。EGFR 突变肿瘤的偏度最高，ALK 重排肿瘤的偏度最低，野生型肿瘤的偏度居中。在独立嵌套验证集中计算的模型平均准确率为 81.76%（95%CI81.45-82.06）。
纹理分析，特别是偏度值，可能是一种很有前途的非侵入性方法，用于对肺腺癌的 EGFR 和 ALK 突变进行特征描述。
