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- W3216960398 abstract "Abstract Purpose To explore CT radiomics for predicting programmed cell death ligand 1 on tumor cells (PD-L1) status in gastric cancer (GC). Methods From March 2019 to July 2020, 358 patients identified with GC who underwent surgery at our hospital were enrolled in this study retrospectively. All patients were divided into primary (n=239) and validation (n=119) cohorts based on the time of surgery at a ratio of 3:1. Radiomic features were extracted from regions of interest manually drawn on venous CT images. Besides, preoperative tumor markers of all patients were collected and analyzed. The signatures based on radiomics were built using Support Vector Machine (SVM) and Random Forest (RF). Receiver operating characteristic (ROC) curve was performed to assess diagnostic efficiency. Decision curve analysis confirmed the clinical utility. Results Numerous radiomic features (all p <0.05) differed significantly between GCs with different PD-L1 status. The model developed by SVM algorithm in the primary and validation cohort achieved better performance with AUCs of 0.704 and 0.799, respectively. Conclusion It was promising to predict PD-L1 status in GCs noninvasively using CT radiomics. It might help to improve clinical decision making with regard to immunotherapy." @default.
- W3216960398 created "2021-12-06" @default.
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- W3216960398 date "2021-11-29" @default.
- W3216960398 modified "2023-09-26" @default.
- W3216960398 title "CT radiomics for predicting PD-L1 expression on tumor cells in gastric cancer" @default.
- W3216960398 doi "https://doi.org/10.21203/rs.3.rs-52520/v3" @default.
- W3216960398 hasPublicationYear "2021" @default.
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