A novel maximum wall thickness (Tmax) category for cystic lung adenocarcinomas: a retrospective study focusing on pathological invasiveness and prognosis

Background: Cystic lung adenocarcinoma (CLA) is a major subtype of cystic lung cancer (CLC). Clinical T staging for CLA is ambiguous due to cystic components. This issue impedes precise assessment. Previous research has confirmed the significant predictive value of cystic morphology. This study explores a new category for better CLA prognostic stratification, aiding precision treatment. Methods: Diagnosed CLAs were classified into preinvasive (adenocarcinoma in situ, or minimally invasive adenocarcinoma) and invasive adenocarcinomas. Radiologic analysis focused on cystic-airspace features. Logistic and Cox regression were used to identify independent risk factors for pathological invasiveness and prognosis. A novel category incorporating maximum wall thickness (Tmax) was proposed, and Kaplan-Meier survival analysis validated this approach. Results: A total of 219 patients were included in the study. Pleural traction [odds ratio (OR) =4.76] and Tmax (OR =62.03) were independent risk factors for pathological invasiveness of CLAs. Irregular cystic morphology [hazard ratio (HR) =3.77] and Tmax (HR =7.39) were independent prognostic factors for CLAs. Multiloculation (HR =0.38) was associated with a potential protective effect. A novel Tmax category was first proposed: Tmax① ≤0.5 cm; 0.5 cm < Tmax② ≤1.0 cm; 1.0 cm < Tmax③ ≤1.3 cm; Tmax④ >1.3 cm. Kaplan-Meier survival analysis demonstrated highly significant prognostic stratification in 5-year disease-free survival (DFS) and overall survival (OS) across Tmax subgroups. Conclusions: The Tmax variable will serve as the most critical indicator for evaluating the invasiveness and prognosis of CLAs. A novel Tmax category has been established. Its predictive model effectively reflects pathological invasiveness and prognostic outcomes in patients. This Tmax category is proven beneficial for clinical surgeons in conducting staged diagnoses and other applications.