Prediction Model for Tumor Volume Nadir in EGFR-mutant NSCLC Patients Treated With EGFR Tyrosine Kinase Inhibitors

Abstract

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).Materials and Methods: 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.Results: 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 ; R 2 = 0.916). The model was tested in the validation cohort, resulting in the R 2 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. Conclusion: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.