A clinicoradiological model based on clinical and CT features for preoperative prediction of histological classification in patients with epithelial ovarian cancers: a two-center study.
Objectives: To develop and validate a clinicoradiological model integrating clinical and computed tomography (CT) features to preoperative predict histological classification in patients with epithelial ovarian cancers (EOCs). Methods: This retrospective study included 470 patients who were patholog...
| Publicado en: | Abdominal Radiology Vol. 50; no. 9; pp. 4331 - 4342 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
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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=187189744&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187189744 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2025 vid: 50 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187189744 183159919 10.1007/s00261-025-04842-x 187189744 ppf: 4331 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A clinicoradiological model based on clinical and CT features for preoperative prediction of histological classification in patients with epithelial ovarian cancers: a two-center study. aug: au: Li, Jiaojiao Wang, Wenjiang Zhang, Bin Zhu, Xiaolong Liu, Di Li, Chuangui Wang, Fang Cui, Shujun Ye, Zhaoxiang affil: https://ror.org/03hqwnx39 Department of Radiology, First Affiliated Hospital of Hebei North University, Zhangjiakou, China sug: ab: Objectives: To develop and validate a clinicoradiological model integrating clinical and computed tomography (CT) features to preoperative predict histological classification in patients with epithelial ovarian cancers (EOCs). Methods: This retrospective study included 470 patients who were pathologically proven EOCs and performed by contrast enhanced CT before treatment from center I (training cohort, N = 329; internal test cohort, N = 141) and 83 EOC patients who were included as an external test cohort from center II. The univariate analysis and multivariate logistic regression analysis were used to select significant clinical and CT features. The significant clinical model was developed based on clinical characteristics. The significant radiological model was established by CT features. The significant clinical and CT features were used to construct the clinicoradiological model. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, the Brier score and decision curve analysis (DCA). The AUCs were compared by net reclassification index (NRI) and integrated discrimination improvement (IDI). Results: The significant clinical and CT parameters including age, transverse diameter, morphology, margin, ascites and lymphadenopathy were incorporated to build the clinicoradioligical model. The clinicoradiological model showed relatively satisfactory discrimination between type I and type II EOCs with the AUC of 0.841 (95% confidence interval [CI] 0.797–0.886), 0.874 (95% CI 0.811–0.937) and 0.826 (95% CI 0.729–0.923) in the training, internal and external test cohorts, respectively. The NRI and IDI showed the clinicoradiological model significantly performed than those of the clinical model (all P < 0.05). No statistical significance was found between radiological and clinicoradiological model. The clinicoradiological model demonstrated optimal classification accuracy and clinical application value. Conclusion: The easily accessible nomogram based on the clinicoradiologic model showed favorable performance in distinguishing between type I and type II EOCs and could therefore be used to improve the clinical management of EOC patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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