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      Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging

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          Abstract

          Background: To develop and validate a radiomic nomogram incorporating radiomic features with clinical variables for individual local recurrence risk assessment in nasopharyngeal carcinoma (NPC) patients before initial treatment.

          Methods: One hundred and forty patients were randomly divided into a training cohort (n = 80) and a validation cohort (n = 60). A total of 970 radiomic features were extracted from pretreatment magnetic resonance (MR) images of NPC patients from May 2007 to December 2013. Univariate and multivariate analyses were used for selecting radiomic features associated with local recurrence, and multivariate analyses was used for building radiomic nomogram.

          Results: Eight contrast-enhanced T1-weighted (CET1-w) image features and seven T2-weighted (T2-w) image features were selected to build a Cox proportional hazard model in the training cohort, respectively. The radiomic nomogram, which combined radiomic features and multiple clinical variables, had a good evaluation ability (C-index: 0.74 [95% CI: 0.58, 0.85]) in the validation cohort. The radiomic nomogram successfully categorized those patients into low- and high-risk groups with significant differences in the rate of local recurrence-free survival ( P <0.05).

          Conclusions: This study demonstrates that MR imaging-based radiomics can be used as an aid tool for the evaluation of local recurrence, in order to develop tailored treatment targeting specific characteristics of individual patients.

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          Most cited references35

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          A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

          This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.
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            Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

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              Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study

              Gene expression patterns can be used as prognostic biomarkers in various types of cancers. We aimed to identify a gene expression pattern for individual distant metastatic risk assessment in patients with locoregionally advanced nasopharyngeal carcinoma.
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                Author and article information

                Journal
                J Cancer
                J Cancer
                jca
                Journal of Cancer
                Ivyspring International Publisher (Sydney )
                1837-9664
                2019
                10 July 2019
                : 10
                : 18
                : 4217-4225
                Affiliations
                [1 ]Department of Radiology, Guangdong Provincial People's Hospital/ Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China
                [2 ]Institute of Automation, Chinese Academy of Sciences, CAS Key Laboratory of Molecular Imaging, Beijing, PR China
                [3 ]Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China
                [4 ]Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China
                [5 ]Affiliated Hospital of Guizhou Medical University, Guiyang, Department of Radiology Guiyang, Guizhou, PR China
                Author notes
                ✉ Corresponding authors: Shuixing Zhang and Zhouyang Lian. Department of Radiology, Guangdong Provincial People's Hospital/ Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080 Guangzhou, Guangdong Prov., People's Republic of China. E-mail: zsx7515@ 123456jnu.edu.cn ; Tel: +86 20 83870125; Fax: +86 20 83870125

                *Lu Zhang, Hongyu Zhou, Dongsheng Gu, and Jie Tian contributed equally to this work.

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                jcav10p4217
                10.7150/jca.33345
                6691694
                31413740
                4b2d6544-cc2b-4806-89a5-eb783f6af87c
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 20 January 2019
                : 25 May 2019
                Categories
                Research Paper

                Oncology & Radiotherapy
                magnetic resonance imaging,nasopharyngeal carcinoma,local recurrence,radiomic feature,nomogram

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