Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma.
Objectives: To develop and validate an optimal model based on the 1-mm-isotropic-3D contrast-enhanced StarVIBE MRI sequence combined with clinical risk factors for predicting survival in patients with esophageal squamous cell carcinoma (ESCC).Methods: Patients with ESCC at our institution from 2015...
| Publicado en: | European Radiology Vol. 32; no. 9; pp. 5930 - 5943 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research randomized controlled trial Journal Article |
| Publicado: |
Springer Nature
Sep2022
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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=158547050&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158547050 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2022 vid: 32 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158547050 158547050 NLM35384460 158547050 10.1007/s00330-022-08776-6 NLM35384460 158547050 ppf: 5930 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma. aug: au: Chu, Funing Liu, Yun Liu, Qiuping Li, Weijia Jia, Zhengyan Wang, Chenglong Wang, Zhaoqi Lu, Shuang Li, Ping Zhang, Yuanli Liao, Yubo Xu, Mingzhe Yao, Xiaoqiang Wang, Shuting Liu, Cuicui Zhang, Hongkai Wang, Shaoyu Yan, Xu Kamel, Ihab R. Sun, Haibo affil: Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, 450008, Zhengzhou, Henan, China sug: subj: Esophageal Neoplasms Overall Survival Magnetic Resonance Imaging Methods Prognosis Human Retrospective Design Models, Statistical Comparative Studies Multicenter Studies Randomized Controlled Trials Evaluation Research Validation Studies Funding Source ab: Objectives: To develop and validate an optimal model based on the 1-mm-isotropic-3D contrast-enhanced StarVIBE MRI sequence combined with clinical risk factors for predicting survival in patients with esophageal squamous cell carcinoma (ESCC).Methods: Patients with ESCC at our institution from 2015 to 2017 participated in this retrospective study based on prospectively acquired data, and were randomly assigned to training and validation groups at a ratio of 7:3. Random survival forest (RSF) and variable hunting methods were used to screen for radiomics features and LASSO-Cox regression analysis was used to build three models, including clinical only, radiomics only and combined clinical and radiomics models, which were evaluated by concordance index (CI) and calibration curve. Nomograms and decision curve analysis (DCA) were used to display intuitive prediction information.Results: Seven radiomics features were selected from 434 patients, combined with clinical features that were statistically significant to construct the predictive models of disease-free survival (DFS) and overall survival (OS). The combined model showed the highest performance in both training and validation groups for predicting DFS ([CI], 0.714, 0.729) and OS ([CI], 0.730, 0.712). DCA showed that the net benefit of the combined model and of the clinical model is significantly greater than that of the radiomics model alone at different threshold probabilities.Conclusions: We demonstrated that a combined predictive model based on MR Rad-S and clinical risk factors had better predictive efficacy than the radiomics models alone for patients with ESCC.Key Points: • Magnetic resonance-based radiomics features combined with clinical risk factors can predict survival in patients with ESCC. • The radiomics nomogram can be used clinically to predict patient recurrence, DFS, and OS. • Magnetic resonance imaging is highly reproducible in visualizing lesions and contouring the whole tumor. pubtype: Academic Journal doctype: research randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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