Zhao Shijun, Hou Donghui, Zheng Xiaomin, Song Wei, Liu Xiaoqing, Wang Sicong, Zhou Lina, Tao Xiuli, Lv Lv, Sun Qi, Jin Yujing, Ding Lieming, Mao Li, Wu Ning
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Endocrinology, Chui Yang Liu Hospital affiliated to Tsinghua University, Beijing, China.
Transl Lung Cancer Res. 2021 Jan;10(1):368-380. doi: 10.21037/tlcr-20-361.
Intracranial progression is considered an important cause of treatment failure in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients. Recent advances in targeted therapy and radiomics have generated considerable interest for the exploration of prognostic imaging biomarkers to predict the clinical course. Here, we developed a magnetic resonance imaging (MRI) radiomic signature that can stratify survival and intracranial progression.
We analyzed 87 brain metastatic lesions in 24 ALK-positive NSCLC patients undergoing ALK-inhibitor ensartinib therapy and divided them into training (n=61) and validation (n=26) sets. Radiomic features were extracted and screened from contrast-enhanced MR images. Combined with these selected features, the Rad-score was calculated with multivariate logistic regression. The predictive model and Rad-score performance were assessed in the training set and validated in the validation set; decision curve analysis was performed with the combined training and validation sets to estimate Rad-score's patient-stratification ability.
The prediction model constructed with nine selected radiomic features could predict intracranial progression within 51 weeks (AUC =0.84 and 0.85 in the training and validation sets, respectively), while clinical and regular MRI characteristics were independent of progression (P>0.05). The decision-curve analysis showed that the radiomic prediction model was clinically useful. The Kaplan-Meier analysis showed that the progression-free survival (PFS) difference between the high- and low-risk groups distinguished by the Rad-score was significant (P=0.017).
Radiomics may provide prognostic information and improve pretreatment risk stratification in ALK-positive NSCLC patients with brain metastases undergoing ensartinib treatment, allowing follow-up and treatment to be tailored to the patient's individual risk profile.
颅内进展被认为是间变性淋巴瘤激酶（ALK）阳性非小细胞肺癌（NSCLC）患者治疗失败的重要原因。靶向治疗和放射组学的最新进展引发了人们对探索预测临床病程的预后影像生物标志物的浓厚兴趣。在此，我们开发了一种磁共振成像（MRI）放射组学特征，可对生存情况和颅内进展进行分层。
我们分析了24例接受ALK抑制剂恩沙替尼治疗的ALK阳性NSCLC患者的87个脑转移瘤病灶，并将其分为训练集（n = 61）和验证集（n = 26）。从对比增强MR图像中提取并筛选放射组学特征。结合这些选定特征，通过多变量逻辑回归计算Rad评分。在训练集中评估预测模型和Rad评分性能，并在验证集中进行验证；使用训练集和验证集的合并集进行决策曲线分析，以评估Rad评分的患者分层能力。
由九个选定的放射组学特征构建的预测模型可以预测51周内的颅内进展（训练集和验证集的AUC分别为0.84和0.85），而临床和常规MRI特征与进展无关（P>0.05）。决策曲线分析表明，放射组学预测模型具有临床实用性。Kaplan-Meier分析表明，由Rad评分区分的高风险组和低风险组之间的无进展生存期（PFS）差异显著（P = 0.017）。
放射组学可能为接受恩沙替尼治疗的ALK阳性NSCLC脑转移患者提供预后信息并改善治疗前风险分层，从而使随访和治疗能够根据患者的个体风险特征进行调整。
