Construction of a prediction efficacy model for PD-1 inhibitor in advanced esophageal squamous cell carcinoma
- VernacularTitle:PD-1抑制剂用于晚期食管鳞状细胞癌的疗效预测模型构建
- Author:
Shanshan WU
1
;
Xiaojie HUANG
1
;
Xiaochun XIE
1
;
Shaokai HUANG
1
;
Lina HUANG
1
;
Xiaofen WANG
2
Author Information
1. Dept. of Pharmacy，Jieyang People’s Hospital，Guangdong Jieyang 522000，China
2. Dept. of Oncology，Jieyang People’s Hospital，Guangdong Jieyang 522000，China
- Publication Type:Journal Article
- Keywords:
esophageal squamous cell carcinoma;
PD-1
- From:
China Pharmacy
2025;36(17):2154-2159
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To develop a prediction model for durable clinical benefit （DCB） in patients with advanced esophageal squamous cell carcinoma （ESCC） receiving programmed death-1 （PD-1） inhibitor. METHODS The clinical data of patients with advanced ESCC who received PD-1 inhibitor in Jieyang People’s Hospital were retrospectively collected between January 2020 to December 2023. Predictors were screened by least absolute shrinkage and selection operator （Lasso） regression， and a multivariable Logistic regression model was developed to predict DCB. A nomogram was constructed based on the model. Internal validation of the prediction model was performed by using the Bootstrap method， and the model was evaluated by the receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis. RESULTS A total of 91 patients with advanced ESCC were included. The results of Lasso regression combined with Logistic regression analysis indicated that the baseline lymphocyte monocyte ratio （LMR） [odds ratio （OR）＝1.97， 95% confidence interval （CI）： 1.15-3.36， P＝0.013]， albumin （ALB） content （OR＝1.35， 95%CI： 1.13-1.60， P＜0.001）， body mass index （BMI） category 1 [normal vs. low： OR＝ 0.28， 95%CI （0.09-0.96）， P＝0.042]， BMI category 2 [overweight-obesity vs. low： OR＝0.08， 95%CI （0.01-0.59）， P＝0.013]， and treatment regimen [monotherapy vs. monotherapy combination therapy： OR＝0.07， 95%CI （0.01-0.50）， P＝0.008] were predictive factors for patients with advanced ESCC to achieve DCB when treated with PD-1 inhibitor. A prediction model was constructed based on the above indicators. Internal validation of the model using the Bootstrap method showed an area under the curve of 0.831 （95%CI： 0.746-0.904）， with specificity of 74.4% and sensitivity of 75.0%. The Hosmer-Lemeshow test yielded χ2＝ 9.930， P＝0.270， and the calibration curve slope was close to 1. The decision curve analysis demonstrated that the model exhibited good clinical utility within a threshold range of 0.1 to 1.0. CONCLUSIONS The prediction model based on baseline LMR， ALB content， BMI， and treatment regimen demonstrates robust predictive performance and clinical utility for assessing therapeutic efficacy of PD-1 inhibitor in the treatment of advanced ESCC.
