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Updated: Jun 18, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Radiomics Models Derived From Arterial-Phase-Enhanced CT Reliably Predict Both PD-L1 Expression and Immunotherapy
Zhenhua Liu1, Yimin Yao2, Miaomiao Zhao3
1Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China; Department of Radiotherapy, Yancheng First Hospital Affiliated Hospital of Nanjing University Medical School, The First people's Hospital of Yancheng, 66 Renmin Road, Yancheng 224005, China; National Clinical Research Center for Hematologic Diseases, Cyrus Tang Medical Institute, Collaborative Innovation Center of Hematology, State Key Laboratory of Radiation Medicine and Prevention, Soochow University, Suzhou 215123, China.
Rationale And Objectives:
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC) and programmed cell death-ligand 1 (PD-L1) is a companion biomarker. This study aims to use baseline arterial-phase enhanced CT (APECT) to construct efficient radiomic models for predicting PD-L1 expression and immunotherapy prognosis in NSCLC.
Materials And Methods:
We extracted radiomics features from the baseline APECT images of 204 patients enrolled in a published multicenter clinical trial that commenced on August 23, 2018, and concluded on November 15, 2019 (ClinicalTrials.gov: NCT03607539). Of these patients, 146 patients from selected centers were assigned to the training cohort. The least absolute shrinkage and selection operator (LASSO) method was used to reduce dimensionality of radiomics features and calculate tumor scores. Models were created using naive bayes, decision trees, XGBoost, and random forest algorithms according to tumor scores. These models were then validated in an independent validation cohort comprising 58 patients from the remaining centers.
Results:
The random forest algorithm outperformed the other methods. In the three-classification scenario, the random forest model achieving the area under the curve (AUC) values of 0.98 and 0.94 in the training and validation cohorts, respectively. In the two-classification scenario, the random forest model achieved AUCs of 0.99 (95%CI: 0.97-1.0, P < 0.0001) and 0.93 (95%CI: 0.83-0.98, P < 0.0001) in the training and validation cohorts, respectively. Furthermore, patients classified as PD-L1 high-expression by this model can predict treatment response (AUC=0.859, 95%CI: 0.7-0.96, P < 0.001) and improved survival (HR=0.2, 95%CI: 0.08-0.53, P = 0.001) only in validation sintilimab arm.
Conclusion:
Radiomics models based on APECT represent a potential non-invasive approach to robustly predict PD-L1 expression and ICI treatment outcomes in patients with NSCLC, which could significantly improve precision cancer immunotherapy.
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