Purpose: We established a computed tomography (CT)-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKIs) therapy in multicenter, stage IV EGFR-mutated non-small-cell lung cancer (NSCLC) patients. Experimental Design: 1032 CT-based phenotypic characteristics were extracted according to the intensity, shape and texture of NSCLC pre-therapy images. Based on these CT features extracted from 117 stage IV EGFR-mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV EGFR-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts. Results: The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid- and slow-progression to EGFR-TKI therapy in three cohorts (hazard ratio: 3.61, 3.77 and 3.67, respectively). Rapid-progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit (p = 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model (p < 0.0001). Conclusions:The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pre-therapy personalized management of TKIs.
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