Can PD-L1 expression be predicted by contrast-enhanced CT in patients with gastric adenocarcinoma? a preliminary retrospective study.
Background: This study aimed to construct a computed tomography (CT) radiomics model to predict programmed cell death-ligand 1 (PD-L1) expression in gastric adenocarcinoma patients using radiomics features. Methods: A total of 169 patients with gastric adenocarcinoma were studied retrospectively and...
| Publicado en: | Abdominal Radiology Vol. 48; no. 1; pp. 220 - 229 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161360674&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161360674 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2023 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161360674 159782695 10.1007/s00261-022-03709-9 161360674 ppf: 220 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Can PD-L1 expression be predicted by contrast-enhanced CT in patients with gastric adenocarcinoma? a preliminary retrospective study. aug: au: Gu, Xiaolong Yu, Xianbo Shi, Gaofeng Li, Yang Yang, Li affil: Department of Radiology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, People's Republic of China sug: ab: Background: This study aimed to construct a computed tomography (CT) radiomics model to predict programmed cell death-ligand 1 (PD-L1) expression in gastric adenocarcinoma patients using radiomics features. Methods: A total of 169 patients with gastric adenocarcinoma were studied retrospectively and randomly divided into training and testing datasets. The clinical data of the patients were recorded. Radiomics features were extracted to construct a radiomics model. The random forest-based Boruta algorithm was used to screen the features of the training dataset. A receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model. Results: Four radiomics features were selected to construct a radiomics model. The radiomics signature showed good efficacy in predicting PD-L1 expression, with an area under the receiver operating characteristic curve (AUC) of 0.786 (p < 0.001), a sensitivity of 0.681, and a specificity of 0.826. The radiomics model achieved the greatest areas under the curve (AUCs) in the training dataset (AUC = 0.786) and testing dataset (AUC = 0.774). The calibration curves of the radiomics model showed great calibration performances outcomes in the training dataset and testing dataset. The net clinical benefit for the radiomics model was high. Conclusion: CT radiomics has important value in predicting the expression of PD-L1 in patients with gastric adenocarcinoma. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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