Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Sci Rep. 2021 May 11;11(1):9984. doi: 10.1038/s41598-021-88239-y.
Among non-small cell lung cancer (NSCLC) patients with therapeutically targetable tumor mutations in epidermal growth factor receptor (EGFR), not all patients respond to targeted therapy. Combining circulating-tumor DNA (ctDNA), clinical variables, and radiomic phenotypes may improve prediction of EGFR-targeted therapy outcomes for NSCLC. This single-center retrospective study included 40 EGFR-mutant advanced NSCLC patients treated with EGFR-targeted therapy. ctDNA data included number of mutations and detection of EGFR T790M. Clinical data included age, smoking status, and ECOG performance status. Baseline chest CT scans were analyzed to extract 429 radiomic features from each primary tumor. Unsupervised hierarchical clustering was used to group tumors into phenotypes. Kaplan-Meier (K-M) curves and Cox proportional hazards regression were modeled for progression-free survival (PFS) and overall survival (OS). Likelihood ratio test (LRT) was used to compare fit between models. Among 40 patients (73% women, median age 62 years), consensus clustering identified two radiomic phenotypes. For PFS, the model combining radiomic phenotypes with ctDNA and clinical variables had c-statistic of 0.77 and a better fit (LRT p = 0.01) than the model with clinical and ctDNA variables alone with a c-statistic of 0.73. For OS, adding radiomic phenotypes resulted in c-statistic of 0.83 versus 0.80 when using clinical and ctDNA variables (LRT p = 0.08). Both models showed separation of K-M curves dichotomized by median prognostic score (p < 0.005). Combining radiomic phenotypes, ctDNA, and clinical variables may enhance precision oncology approaches to managing advanced non-small cell lung cancer with EGFR mutations.
在表皮生长因子受体（EGFR）治疗靶点肿瘤突变的非小细胞肺癌（NSCLC）患者中，并非所有患者都对靶向治疗有反应。结合循环肿瘤 DNA（ctDNA）、临床变量和放射组学表型可能会提高 EGFR 靶向治疗 NSCLC 结果的预测能力。这项单中心回顾性研究纳入了 40 名接受 EGFR 靶向治疗的 EGFR 突变晚期 NSCLC 患者。ctDNA 数据包括突变数量和 EGFR T790M 的检测。临床数据包括年龄、吸烟状况和 ECOG 表现状态。对基线胸部 CT 扫描进行分析，从每个原发肿瘤中提取 429 个放射组学特征。采用无监督层次聚类将肿瘤分为表型。绘制 Kaplan-Meier（K-M）曲线和 Cox 比例风险回归模型，以评估无进展生存期（PFS）和总生存期（OS）。采用似然比检验（LRT）比较模型之间的拟合优度。在 40 名患者（73%为女性，中位年龄 62 岁）中，共识聚类确定了两种放射组学表型。对于 PFS，联合放射组学表型与 ctDNA 和临床变量的模型具有 0.77 的 C 统计量，拟合度优于仅包含临床和 ctDNA 变量的模型（LRT p=0.01），后者的 C 统计量为 0.73。对于 OS，当使用临床和 ctDNA 变量时，添加放射组学表型会导致 C 统计量从 0.80 变为 0.83（LRT p=0.08）。两种模型的 K-M 曲线均根据中位预后评分分为两组（p<0.005）。联合放射组学表型、ctDNA 和临床变量可能会提高管理 EGFR 突变的晚期非小细胞肺癌的精准肿瘤学方法的精度。
