Jiang Meilin, Yang Pei, Li Jing, Peng Wenying, Pu Xingxiang, Chen Bolin, Li Jia, Wang Jingyi, Wu Lin
The Second Department of Thoracic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
The General Surgery Department of Xiangya Hospital Affiliated to Central South University, Changsha, China.
Front Oncol. 2022 Aug 16;12:985284. doi: 10.3389/fonc.2022.985284. eCollection 2022.
Biomarkers that predict the efficacy of first-line tyrosine kinase inhibitors (TKIs) are pivotal in epidermal growth factor receptor (EGFR) mutant advanced lung adenocarcinoma. Imaging-based biomarkers have attracted much attention in anticancer therapy. This study aims to use the machine learning method to distinguish EGFR mutation status and further explores the predictive role of EGFR mutation-related radiomics features in response to first-line TKIs.
We retrospectively analyzed pretreatment CT images and clinical information from a cohort of lung adenocarcinomas. We entered the top-ranked features into a support vector machine (SVM) classifier to establish a radiomics signature that predicted EGFR mutation status. Furthermore, we identified the best response-related features based on EGFR mutant-related features in first-line TKI therapy patients. Then we test and validate the predictive effect of the best response-related features for progression-free survival (PFS).
Six hundred ninety-two patients were enrolled in building radiomics signatures. The 13 top-ranked features were input into an SVM classifier to establish the radiomics signature of the training cohort (n = 514), and the predictive score of the radiomics signature was assessed on an independent validation group with 178 patients and obtained an area under the curve (AUC) of 74.13%, an F1 score of 68.29%, a specificity of 79.55%, an accuracy of 70.79%, and a sensitivity of 62.22%. More importantly, the skewness-Low (≤0.882) or 10th percentile-Low group (≤21.132) had a superior partial response (PR) rate than the skewness-High or 10th percentile-High group ( < 0.01). Higher skewness (hazard ratio (HR) = 1.722, = 0.001) was also found to be significantly associated with worse PFS.
The radiomics signature can be used to predict EGFR mutation status. Skewness may contribute to the stratification of disease progression in lung cancer patients treated with first-line TKIs.
预测一线酪氨酸激酶抑制剂（TKIs）疗效的生物标志物在表皮生长因子受体（EGFR）突变的晚期肺腺癌中至关重要。基于影像的生物标志物在抗癌治疗中备受关注。本研究旨在使用机器学习方法区分EGFR突变状态，并进一步探索EGFR突变相关的放射组学特征在一线TKIs反应中的预测作用。
我们回顾性分析了一组肺腺癌患者的治疗前CT图像和临床信息。我们将排名靠前的特征输入支持向量机（SVM）分类器，以建立预测EGFR突变状态的放射组学特征。此外，我们根据一线TKI治疗患者中EGFR突变相关特征确定了最佳反应相关特征。然后我们测试并验证最佳反应相关特征对无进展生存期（PFS）的预测效果。
692例患者被纳入构建放射组学特征。将排名前13的特征输入SVM分类器，以建立训练队列（n = 514）的放射组学特征，并在一个由178例患者组成的独立验证组中评估放射组学特征的预测分数，得到曲线下面积（AUC）为74.13%，F1分数为68.29%，特异性为79.55%，准确性为70.79%，敏感性为62.22%。更重要的是，低偏度（≤0.882）或第10百分位数低分组（≤21.132）的部分缓解（PR）率高于高偏度或第10百分位数高分组（<0.01）。还发现较高的偏度（风险比（HR）= 1.722， = 0.001）与更差的PFS显著相关。
放射组学特征可用于预测EGFR突变状态。偏度可能有助于对接受一线TKIs治疗的肺癌患者的疾病进展进行分层。
