Departments of Imaging.
Departments of Radiology.
J Thorac Imaging. 2023 Mar 1;38(2):82-87. doi: 10.1097/RTI.0000000000000615. Epub 2021 Sep 15.
In patients with advanced non-small cell lung cancer (NSCLC) and oncogenic driver mutations treated with effective targeted therapy, a characteristic pattern of tumor volume dynamics with an initial regression, nadir, and subsequent regrowth is observed on serial computed tomography (CT) scans. We developed and validated a linear model to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR tyrosine kinase inhibitors (TKI).
Patients with EGFR -mutant advanced NSCLC treated with EGFR-TKI as their first EGFR-directed therapy were studied for CT tumor volume kinetics during therapy, using a previously validated CT tumor measurement technique. A linear regression model was built to predict tumor volume nadir in a training cohort of 34 patients, and then was validated in an independent cohort of 84 patients.
The linear model for tumor nadir prediction was obtained in the training cohort of 34 patients, which utilizes the baseline tumor volume before initiating therapy (V 0 ) to predict the volume decrease (mm 3 ) when the nadir volume (V p ) was reached: V 0 -V p =0.717×V 0 -1347 ( P =2×10 -16 ; R2 =0.916). The model was tested in the validation cohort, resulting in the R2 value of 0.953, indicating that the prediction model generalizes well to another cohort of EGFR -mutant patients treated with EGFR-TKI. Clinical variables were not significant predictors of tumor volume nadir.
The linear model was built to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR-TKIs, which provide an important metrics in treatment monitoring and therapeutic decisions at nadir such as additional local abrasive therapy.
在接受有效靶向治疗的晚期非小细胞肺癌（NSCLC）和致癌驱动突变患者中，连续计算机断层扫描（CT）上观察到肿瘤体积动态具有初始消退、最低点和随后再生长的特征模式。我们开发并验证了一种线性模型，用于预测接受表皮生长因子受体酪氨酸激酶抑制剂（TKI）治疗的表皮生长因子受体（EGFR）突变型晚期 NSCLC 患者的肿瘤体积最低点。
对接受 EGFR-TKI 作为其首次 EGFR 定向治疗的 EGFR 突变型晚期 NSCLC 患者进行 CT 肿瘤体积动力学研究，使用先前验证的 CT 肿瘤测量技术。建立了线性回归模型，以预测 34 例患者的训练队列中的肿瘤体积最低点，然后在 84 例患者的独立队列中进行验证。
在 34 例患者的训练队列中获得了肿瘤最低点预测的线性模型，该模型利用开始治疗前的基线肿瘤体积（V0）来预测最低点体积（Vp）时的体积下降（mm3）：V0-Vp=0.717×V0-1347（P=2×10-16；R2=0.916）。该模型在验证队列中进行了测试，得到的 R2 值为 0.953，表明预测模型很好地推广到接受 EGFR-TKI 治疗的另一组 EGFR 突变型患者。临床变量不是肿瘤体积最低点的显著预测因子。
建立了一种线性模型来预测接受 EGFR-TKI 治疗的 EGFR 突变型晚期 NSCLC 患者的肿瘤体积最低点，这为治疗监测和治疗决策提供了一个重要的指标，例如最低点时的额外局部侵蚀性治疗。
