Journal of multidisciplinary healthcare · 2025
Role of Computed Tomography in Predicting Programmed Death Ligand-1 Positivity in Gastric Adenocarcinoma.
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Yu Zhi-Hong, Ma Wei-Qin, Ren Ji-Wei, Zhang Xu-Ting, Chu Lin
Аннотация
OBJECTIVE: To examine the association between computed tomography (CT) imaging characteristics and programmed death ligand-1 (PD-L1) expression in patients with gastric adenocarcinoma (GAC), and to develop a nomogram model for prediction.
METHODS: The patients were randomly allocated into a training set and a validation set at a ratio of 7:3. The training set was further divided into a PD-L1 positive group and a PD-L1 negative group, based on the combined positive score (CPS). Univariate and multivariate logistic regression analyses were performed to identify independent predictors of PD-L1 positivity. A nomogram was developed to assess the model's predictive performance, which was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). It was also compared with the model established by previous study.
RESULTS: Patients with PD-L1-positive gastric adenocarcinoma exhibited a higher prevalence of larger short diameters of lymph nodes (LNs) (≥ 1 cm), and lower CT attenuation values in the venous and delayed phases compared to those in the PD-L1-negative group. Short diameter of LNs, and CT attenuation values in the delayed phase were identified as independent predictors of PD-L1 positivity. The nomogram analysis indicated that CT attenuation values in the delayed phase were the most significant predictor of PD-L1 positivity, followed by short diameter of LNs.
CONCLUSION: The GAC prediction model based on the CT imaging features is effective in predicting PD-L1 expression levels and demonstrates strong clinical applicability.
METHODS: The patients were randomly allocated into a training set and a validation set at a ratio of 7:3. The training set was further divided into a PD-L1 positive group and a PD-L1 negative group, based on the combined positive score (CPS). Univariate and multivariate logistic regression analyses were performed to identify independent predictors of PD-L1 positivity. A nomogram was developed to assess the model's predictive performance, which was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). It was also compared with the model established by previous study.
RESULTS: Patients with PD-L1-positive gastric adenocarcinoma exhibited a higher prevalence of larger short diameters of lymph nodes (LNs) (≥ 1 cm), and lower CT attenuation values in the venous and delayed phases compared to those in the PD-L1-negative group. Short diameter of LNs, and CT attenuation values in the delayed phase were identified as independent predictors of PD-L1 positivity. The nomogram analysis indicated that CT attenuation values in the delayed phase were the most significant predictor of PD-L1 positivity, followed by short diameter of LNs.
CONCLUSION: The GAC prediction model based on the CT imaging features is effective in predicting PD-L1 expression levels and demonstrates strong clinical applicability.