Construction and validation of a recurrence prediction model for lung adenocarcinoma after treatment integrating radiomics features and clinical indicators.

objectiveTo investigate the feasibility and clinical value of constructing a predictive model for post-treatment recurrence risk in lung adenocarcinoma based on radiomics features (including texture and morphological features extracted from pretreatment chest CT images) and clinical indicators (age, gender, smoking history, pathological type, tumor-node-metastasis [TNM] stage, and tumor markers: carcinoembryonic antigen [CEA] and cytokeratin 19 fragment [CYFRA21-1]).

methodsA total of 350 lung adenocarcinoma patients who underwent standardized treatment from January 2020 to September 2024 were retrospectively enrolled and randomly divided into a training set (n = 245) and a validation set (n = 105) in a 7:3 ratio. Clinical data, pretreatment chest CT radiomics features, and tumor marker levels were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors, followed by the construction of a nomogram model. The model’s performance was evaluated and validated.

resultsThe recurrence rates were 33.11% (74/245) in the training set and 33.33% (35/105) in the validation set. Multivariate regression analysis showed that radiomics features (entropy and long axis ratio), TNM stage (III-IV), CEA level (≥ 5 ng/mL), and smoking history were independent risk factors (P < 0.05). The nomogram model demonstrated C-index values of 0.901 and 0.831 in the training and validation sets, respectively. Receiver operating characteristic curve analysis showed that the area under the curve (AUC) values for predicting post-treatment recurrence were 0.901 (95% CI: 0.854–0.948) and 0.831 (95% CI: 0.732–0.930) in the training and validation sets, with sensitivities of 0.846 and 0.682, specificities of 0.860 and 0.840, precision-recall AUCs of 0.886 and 0.812, positive predictive values of 0.821 and 0.793, and negative predictive values of 0.879 and 0.785, respectively. Calibration curves indicated good agreement between predicted and observed recurrence, supported by Hosmer-Lemeshow test P-values of 0.743 and 0.709.

conclusionThe nomogram model incorporating radiomics features and clinical indicators effectively predicts the risk of post-treatment recurrence in lung adenocarcinoma, offering a practical tool for personalized follow-up and intervention strategies.