2024 Asia Conference on Lung Cancer (ACLC) - Poster & ePosters-EP02.18

EP02.18 - Yintao Li

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The study conducted by Ying Li, Junfeng Zhao, and Yintao Li from Shandong Cancer Hospital and Institute, Shandong First Medical University, and Shandong Academy of Medical Sciences aimed to develop a predictive nomogram for patients with advanced non-small cell lung cancer (NSCLC) undergoing immunotherapy rechallenge after experiencing progressive disease (PD). The research involved 352 patients treated between January 2019 and December 2022, who were split into training and validation cohorts to identify significant predictors for progression-free survival (PFS) and overall survival (OS).<br /><br />Both univariate and multivariate analyses were used to analyze clinicopathological factors and radiomic features. The study identified several independent predictive factors for PFS: time from initial immunotherapy to PD (duration), clinical N stage, liver metastasis, post-PD treatment, and radiomic features. For OS, the important predictors included age, duration, clinical N stage, post-PD treatment, and radiomics. These significant factors informed the development of a prognostic nomogram.<br /><br />The nomogram demonstrated strong predictive performance, with area under the curve (AUC) values for 6-, 12-, and 18-month PFS rated at 0.731, 0.809, and 0.878, and for 12-, 18-, and 24-month OS rated at 0.742, 0.782, and 0.868 in the training cohort. In the validation cohort, AUC values were slightly lower but still indicated good discrimination: 0.672, 0.774, 0.826 for PFS at similar intervals and 0.833, 0.705, and 0.762 for OS.<br /><br />In conclusion, the study successfully developed and validated a nomogram combining clinicopathological characteristics and radiomic data to predict outcomes for NSCLC patients receiving immunotherapy rechallenge, suggesting it could serve as an effective tool in clinical settings.

Keywords

predictive nomogram

non-small cell lung cancer

immunotherapy rechallenge

progression-free survival

overall survival

radiomic features

clinicopathological factors

prognostic tool

Shandong Cancer Hospital

advanced NSCLC