CT-based radiomics model for predicting perineural invasion status in gastric cancer.
Purpose: Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. Methods: This retrospective study included 2...
| Publicado en: | Abdominal Radiology Vol. 50; no. 5; pp. 1916 - 1927 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2025
|
| 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=184452416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184452416 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: May2025 vid: 50 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184452416 180692700 10.1007/s00261-024-04673-2 184452416 ppf: 1916 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: CT-based radiomics model for predicting perineural invasion status in gastric cancer. aug: au: Jiang, Sheng Xie, Wentao Pan, Wenjun Jiang, Zinian Xin, Fangjie Zhou, Xiaoming Xu, Zhenying Zhang, Maoshen Lu, Yun Wang, Dongsheng affil: https://ror.org/026e9yy16 Affiliated Hospital of Qingdao University, Qingdao, China sug: ab: Purpose: Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. Methods: This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models. Results: A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779–0.887) and 0.806 (95% CI: 0.628–0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility. Conclusion: We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|