Development of a model to predict high-risk histopathological features in early T-stage lung adenocarcinoma using thin-slice CT and FDG-PET/CT parameters for selecting optimal candidates for sub-lobar resection.

Background: There has been growing interest in the limited resection of early-stage lung cancer. Imaging-based preoperative risk stratification is expected to improve outcomes, but has not yet been well established. This study aimed to develop an imaging-based model for predicting high-risk histopathological features in early T-stage lung adenocarcinoma to identify low-risk patients. Methods: We retrospectively enrolled patients with cTis or cT1 N0M0 resected adenocarcinomas who underwent thin-slice computed tomography (CT) and Results: We analyzed 190 lesions in 174 patients for the training set, and 34 lesions in 28 patients for the external test set. The parameters vCTR and SUVmax showed the highest area under the receiver operating characteristics curve (AUC) values for risk-positive lesions (vCTR >58%: 0.83; SUVmax >2.0: 0.87) as independent predictors. The AUCs of our model (predictors: vCTR, SUVmax) were 0.87 and 0.85 in the training and external test sets, respectively. Conclusions: We developed and externally validated an imaging-based model for predicting high-risk histopathological features in early T-stage lung adenocarcinoma. This model may prove valuable for identifying suitable candidate patients for limited surgery.