2024 Asia Conference on Lung Cancer (ACLC) - Poster & ePosters-EP02.17

EP02.17 - Yintao Li

Pdf Summary

The study by Junfeng Zhao, Ying Li, and Yintao Li focuses on predicting the prognosis of patients with advanced lung adenocarcinoma treated with third-generation Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors (EGFR-TKI). Significant variability exists in outcomes for these patients post-treatment, prompting the need for improved risk stratification to guide clinical decisions. The research involved 255 patients divided into training and validation cohorts for analysis. <br /><br />The study's aim was to identify prognostic factors influencing progression-free survival (PFS) and overall survival (OS). Univariate and multivariate analyses were employed, incorporating clinicopathologic and radiomic factors. Findings indicated that factors such as neutrophil-lymphocyte ratio, EGFR 21 L858R mutation, brain metastasis, age, and monocyte-lymphocyte ratio were significant predictors of patient outcomes.<br /><br />A nomogram—a graphical tool for predicting cancer prognosis—was developed, showing excellent predictive accuracy. It demonstrated AUCs of 0.810, 0.862, 0.873, and 0.886, 0.881, 0.839 in the training cohort for 6-month, 12-month, and 24-month PFS, and 1-, 2-, and 3-year OS, respectively. Similar robust performance was noted in the validation cohort. The nomogram's calibration indicated a strong concordance between predicted outcomes and actual observations. Furthermore, decision curve analysis highlighted a favorable clinical impact.<br /><br />In conclusion, the nomogram, integrating clinicopathological and radiomic features, provides a valuable prognosis prediction tool, effectively categorizing patients into different risk groups, thereby aiding in personalized treatment planning. This approach promises enhanced prognostic assessment, supporting clinicians in optimizing therapeutic strategies for lung adenocarcinoma patients receiving third-generation EGFR-TKI therapy.

Keywords

lung adenocarcinoma

EGFR-TKI

prognosis prediction

progression-free survival

overall survival

nomogram

risk stratification

radiomic factors

neutrophil-lymphocyte ratio

personalized treatment