Hou Shaoping, Wang Hongbo, Wang Xiaoyu, Chen Huanhuan, Zhou Boyu, Meng Ruiqing, Sha Xianzheng, Chang Shijie, Wang Huan, Jiang Wenyan
School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China.
Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, P.R. China.
Med Phys. 2024 Feb;51(2):1083-1091. doi: 10.1002/mp.16581. Epub 2023 Jul 5.
Preoperative prediction of the epidermal growth factor receptor (EGFR) status in non-small-cell lung cancer (NSCLC) patients with liver metastasis (LM) may have potential clinical values for assisting in treatment decision-making.
To explore the value of tumor-liver interface (TLI)-based magnetic resonance imaging (MRI) radiomics for detecting the EGFR mutation in NSCLC patients with LM.
This retrospective study included 123 and 44 patients from hospital 1 (between Feb. 2018 and Dec. 2021) and hospital 2 (between Nov. 2015 and Aug. 2022), respectively. The patients received contrast-enhanced T1-weighted (CET1) and T2-weighted (T2W) liver MRI scans before treatment. Radiomics features were extracted from MRI images of TLI and the whole tumor region, separately. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the features and establish radiomics signatures (RSs) based on TLI (RS-TLI) and the whole tumor (RS-W). The RSs were evaluated by the receiver operating characteristic (ROC) curve analysis.
A total of 5 and 6 features were identified highly correlated with the EGFR mutation status from TLI and the whole tumor, respectively. The RS-TLI showed better prediction performance than RS-W in the training (AUCs, RS-TLI vs. RS-W, 0.842 vs. 0.797), internal validation (AUCs, RS-TLI vs. RS-W, 0.771 vs. 0.676) and external validation (AUCs, RS-TLI vs. RS-W, 0.733 vs. 0.679) cohort.
Our study demonstrated that TLI-based radiomics can improve prediction performance of the EGFR mutation in lung cancer patients with LM. The established multi-parametric MRI radiomics models may be used as new markers that can potentially assist in personalized treatment planning.
术前预测非小细胞肺癌（NSCLC）肝转移（LM）患者的表皮生长因子受体（EGFR）状态可能对辅助治疗决策具有潜在临床价值。
探讨基于肿瘤-肝脏界面（TLI）的磁共振成像（MRI）放射组学在检测NSCLC肝转移患者EGFR突变中的价值。
这项回顾性研究分别纳入了来自医院1（2018年2月至2021年12月）的123例患者和医院2（2015年11月至2022年8月）的44例患者。患者在治疗前接受了肝脏对比增强T1加权（CET1）和T2加权（T2W）MRI扫描。分别从TLI和整个肿瘤区域的MRI图像中提取放射组学特征。采用最小绝对收缩和选择算子（LASSO）回归筛选特征，并基于TLI（RS-TLI）和整个肿瘤（RS-W）建立放射组学特征（RSs）。通过受试者操作特征（ROC）曲线分析评估RSs。
分别从TLI和整个肿瘤中鉴定出5个和6个与EGFR突变状态高度相关的特征。在训练队列（AUCs，RS-TLI vs. RS-W，0.842对0.797）、内部验证队列（AUCs，RS-TLI vs. RS-W，0.771对0.676）和外部验证队列（AUCs，RS-TLI vs. RS-W，0.733对0.679）中，RS-TLI显示出比RS-W更好的预测性能。
我们的研究表明，基于TLI的放射组学可以提高NSCLC肝转移患者EGFR突变的预测性能。所建立的多参数MRI放射组学模型可作为潜在辅助个性化治疗规划的新标志物。
