Non-invasive prediction of prognostic immune subtypes in lung adenocarcinoma using PET/CT-based radiomics.

Background: The treatment and prognosis of lung adenocarcinoma (LUAD) remain major clinical challenges. While transcriptomic profiling can define immune subtypes, its invasive nature limits clinical utility. Objectives: We aimed to bridge this translational gap by developing and validating a non-invasive Methods: We performed consensus clustering on transcriptomic data from 773 LUAD patients (TCGA/GEO) to identify novel immune subtypes, developed a radiomics-based machine learning classifier using their PET/CT data (n=115) and conducted a biologically and clinically supported external evaluation in an independent cohort from Tianjin Medical University Cancer Hospital (TMUCIH, n=249). In this cohort, we compared immune patterns with transcriptomic subtypes via immunohistochemical staining, and their prognostic significance was evaluated in patients receiving EGFR tyrosine kinase inhibitors (n=118) and immune checkpoint inhibitors (n=75). Results: We identified three robust immune subtypes (log-rank p=0.0017): Inflammatory (ClusterA), Activated (ClusterB), and Evasive (ClusterC). Our radiomics model predicted these subtypes with high accuracy (one-vs-rest AUC = 0.84). Immunohistochemical patterns were consistent with predicted subtypes and stratified overall survival in both the TKI-treated (log-rank p-value=0.041) and ICI-treated (log-rank p-value =0.024) cohorts. These results support a validated, non-invasive approach linking genomics and radiomics to enhance patient stratification and guide personalized treatment strategies in LUAD.