School of Intelligent Medicine, China Medical University, Liaoning, 110122, P.R. China.
Department of Radiology, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Liaoning, 110042, P.R. China.
Acad Radiol. 2023 Jun;30(6):1039-1046. doi: 10.1016/j.acra.2022.06.016. Epub 2022 Jul 27.
The research aims to investigate whether MRI radiomics on hepatic metastasis from primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type, and develop a prediction model based on combination of primary tumor and the metastasis.
A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used to extract radiomics features from intra- and peritumoral areas of both primary tumor and metastasis. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify most predictive features and to develop radiomics signatures (RSs) for prediction of the EGFR mutation status. The receiver operating characteristic (ROC) curve analysis was performed to assess the prediction capability of the developed RSs.
A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis, respectively. The RS-Combine by combination of the primary tumor and metastasis achieved the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant difference between EGFR mutant and wild-type groups (p < 0.05) in the training set.
The study indicates that hepatic metastasis-based radiomics can be used to detect the EGFR mutation. The developed multiorgan combined radiomics signature may be helpful to guide individual treatment strategies for patients with metastatic NSCLC.
本研究旨在探讨基于 MRI 的肝转移瘤影像组学特征是否可用于区分表皮生长因子受体（EGFR）突变型与野生型非小细胞肺癌（NSCLC）患者，并建立基于原发灶和转移灶的预测模型。
回顾性分析 2017 年 8 月至 2021 年 12 月间经病理证实的 130 例肝转移 NSCLC 患者资料，对患者的原发灶和转移灶进行 MRI 扫描，使用 pyradiomics 软件提取肿瘤内及肿瘤旁的影像组学特征。采用最小绝对收缩和选择算子（LASSO）回归筛选出最具预测价值的特征，建立预测 EGFR 基因突变状态的影像组学特征（RS）。采用受试者工作特征（ROC）曲线评估所建立的 RS 的预测效能。
分别基于原发灶和转移灶建立了 RS-Primary 和 RS-Metastasis，将两者联合建立的 RS-Combine 模型在训练集（AUCs：RS-Primary 与 RS-Metastasis 与 RS-Combine，0.826 比 0.821 比 0.908）和测试集（AUCs：RS-Primary 与 RS-Metastasis 与 RS-Combine，0.760 比 0.791 比 0.884）中预测效能最佳。在训练集中，EGFR 突变型和野生型患者的吸烟状态差异有统计学意义（p<0.05）。
基于 MRI 的肝转移瘤影像组学特征可用于检测 EGFR 基因突变。多器官联合的影像组学特征模型可能有助于指导晚期 NSCLC 患者的个体化治疗策略。
