Is the consolidation-to-tumor ratio an independent prognostic factor in clinical T1 stage lung adenocarcinoma radiologically manifesting as a subsolid nodule?—a retrospective cohort study
Abstract
Background: Previous studies indicate that preoperative radiological features, particularly the consolidation-to-tumor ratio (CTR), are associated with prognosis in stage IA lung adenocarcinoma. However, the prognostic value of different CTR thresholds remains controversial in lung adenocarcinoma manifesting as subsolid nodules (SSNs), and whether CTR is an independent prognostic factor has not yet been clarified. Our study aimed to investigate prognostic differences among T1 stage SSN lung adenocarcinoma patients with varying CTR thresholds, maximum solid component diameters (MCDs), and maximum tumor diameters (MTDs). We also sought to explore the independent prognostic value of CTR in this group. Methods: This retrospective study included patients with cT1 stage SSN lung adenocarcinoma who underwent radical resection at The First Affiliated Hospital of Shandong First Medical University between December 2017 and December 2021. The Kaplan-Meier was used to estimate overall survival (OS) and recurrence-free survival (RFS), with the log-rank test comparing survival curves between groups. Firth's penalized Cox regression analysis was employed to identify independent prognostic factors for T1 stage SSN lung adenocarcinoma. Results: edition tumor-node-metastasis (TNM) staging, and MTD groups. In PSN lung adenocarcinoma, only RFS showed statistical differences between the CTR group (cutoff 0.5) and the MCD group (cutoff 20 mm). Stratified analysis revealed that MCD and MTD stratification within the 0.25< CTR 10 mm, and MTD >20 mm are associated with a poorer prognosis in T1 stage SSN lung adenocarcinoma. However, neither CTR, MCD, nor MTD is not an independent prognostic factor for the group; preoperative CEA levels represent more stable and independent prognostic predictors than CTR.
Fields of Study & Subjects
Lung Cancer Diagnosis and Treatment
Lung Cancer Research Studies
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- Journal of Thoracic Disease
- Volume
- 18
- Issue
- 1
- Pages
- 32–32
- Publication date
- 1 January 2026
- Language
- EN