Progression-Free Survival Prediction Model Based on AI-Enhanced Dynamic Radiomics for Personalized EGFR-TKI Treatment Monitoring Patients With Lung Adenocarcinoma

Abstract

Background and Objective: Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the standard first-line treatment for patients with advanced lung adenocarcinoma (LUAD) with EGFR mutations. However, treatment effectiveness varies widely among individuals, and effective models to predict treatment response are lacking. This study aims to establish a progression-free survival (PFS) prediction model based on dynamic changes in pre- and post-treatment CT scans combined with patients' clinical features. Methods: A total of 183 patients with advanced LUAD who received first-line treatment at Peking University Third Hospital from January 2013 to December 2022 were enrolled. A 3D-UNet model was fine-tuned using data from 405 patients with non-small cell lung cancer for advanced lesion segmentation. Clinical and radiomic features extracted using 3D models from 80 EGFR-mutant LUAD patients were used to develop PFS prediction models with a deep-learning binary classification model. The accuracy, specificity, sensitivity, AUC, and F1 score of the models were validated in patients with mutant and wild-type EGFR. Results: In the EGFR-mutant test set (N = 53), the AUC for the 9-month and 12-month progression prediction models were 0.858 (95% CI, 0.707–0.972) and 0.873 (95% CI, 0.747–0.974). Their accuracies were 81.1% (95% CI, 69.8%–90.6%) and 84.9% (95% CI, 73.6%–94.3%), specificities were 87.5% and 72.2%, sensitivities were 80.0% and 91.4%, and F1 scores were 0.878 and 0.889, respectively. Conclusion: This study developed treatment response prediction models for EGFR-mutant LUAD patients. These models demonstrated strong predictive value for PFS in patients treated with EGFR-TKIs, potentially enabling a more efficient personalized CT scan schedule.