| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 8
| Issue : 1 | Page : 27 |
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Development of models for predicting the objective response of immune checkpoint inhibitor therapy in patients with nonsmall-cell lung cancer
Qiao Yang1, Lin Cui2, Yu Gao2, Xiu Yang3, Jianguo Sun3
1 Department of Ultrasound, The 941st Hospital of the PLA Joint Logistic Support Force, Xining, Qinghai, China
2 Department of Emergency, The 941st Hospital of the PLA Joint Logistic Support Force, Xining, Qinghai, China
3 Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, China
Correspondence Address:
Jianguo Sun
Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037
China
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/digm.digm_20_22
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Background: Immune checkpoint inhibitors (ICIs) have significantly improved the therapeutic efficacy and extended the survival of patients with advanced nonsmall-cell lung cancer (NSCLC); however, the benefits were confined to certain populations. How to optimally select eligible patients for ICI therapy needs to be solved. Materials and Methods: A total of 100 NSCLC patients treated with ICIs from two independent studies were included and then were randomly assigned to the train dataset and the validation dataset. A logistic regression model and a decision tree model were developed to predict the objective response outcome. The area under the receiver operating characteristic curves (AUC) of the two models was evaluated. The confusion matrix for decision tree model was also calculated. Results: The comparison of baseline clinical features between the train and validation datasets showed no significant difference. The multivariable logistic regression identified three variables associated with objective response, i.e, age, tumor mutational burden (TMB), and programmed cell death ligand 1. Moreover, a logistic regression model was developed based on these variables. A decision tree, including age and TMB, was also developed. The AUCs of the two models were both 0.85 in the train dataset. And, that was better than each single variable. The AUC of the decision tree in the validation dataset was inferior to that of the logistic regression model. The performance showed that the decision tree could not effectively identify responders. Conclusion: The performance of the decision tree developed in this study was dismal. Its predictive ability was inferior to the logistic regression model.
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