Literature DB >> 28280088

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma.

Bin Zhang1,2, Jie Tian3, Di Dong3, Dongsheng Gu3, Yuhao Dong1,4, Lu Zhang1,2, Zhouyang Lian1,2, Jing Liu1,2, Xiaoning Luo1,2, Shufang Pei1,2, Xiaokai Mo1,4, Wenhui Huang1,5, Fusheng Ouyang1,2, Baoliang Guo1,2, Long Liang1,2, Wenbo Chen6, Changhong Liang1, Shuixing Zhang7.   

Abstract

Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n = 88; validation cohort: n = 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.
Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 × 10-9). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 × 10-7). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages.Conclusions: Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies. Clin Cancer Res; 23(15); 4259-69. ©2017 AACR. ©2017 American Association for Cancer Research.

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Year:  2017        PMID: 28280088     DOI: 10.1158/1078-0432.CCR-16-2910

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  165 in total

1.  Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma.

Authors:  Pengfei Yang; Lei Xu; Zuozhen Cao; Yidong Wan; Yi Xue; Yangkang Jiang; Eric Yen; Chen Luo; Jing Wang; Yi Rong; Tianye Niu
Journal:  Mol Imaging Biol       Date:  2020-12       Impact factor: 3.488

2.  Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.

Authors:  He Zhang; Yunfei Mao; Xiaojun Chen; Guoqing Wu; Xuefen Liu; Peng Zhang; Yu Bai; Pengcong Lu; Weigen Yao; Yuanyuan Wang; Jinhua Yu; Guofu Zhang
Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

3.  CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study.

Authors:  Dongsheng Gu; Yabin Hu; Hui Ding; Jingwei Wei; Ke Chen; Hao Liu; Mengsu Zeng; Jie Tian
Journal:  Eur Radiol       Date:  2019-06-21       Impact factor: 5.315

4.  Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement.

Authors:  Ji Eun Park; Donghyun Kim; Ho Sung Kim; Seo Young Park; Jung Youn Kim; Se Jin Cho; Jae Ho Shin; Jeong Hoon Kim
Journal:  Eur Radiol       Date:  2019-07-26       Impact factor: 5.315

5.  MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma.

Authors:  Lina Zhao; Jie Gong; Yibin Xi; Man Xu; Chen Li; Xiaowei Kang; Yutian Yin; Wei Qin; Hong Yin; Mei Shi
Journal:  Eur Radiol       Date:  2019-08-01       Impact factor: 5.315

6.  An MRI-based radiomics signature as a pretreatment noninvasive predictor of overall survival and chemotherapeutic benefits in lower-grade gliomas.

Authors:  Jingtao Wang; Xuejun Zheng; Jinling Zhang; Hao Xue; Lijie Wang; Rui Jing; Shuo Chen; Fengyuan Che; Xueyuan Heng; Gang Li; Fuzhong Xue
Journal:  Eur Radiol       Date:  2021-01-06       Impact factor: 5.315

7.  Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images.

Authors:  Lihong Peng; Xiaotong Hong; Qingyu Yuan; Lijun Lu; Quanshi Wang; Wufan Chen
Journal:  Ann Nucl Med       Date:  2021-02-04       Impact factor: 2.668

Review 8.  NCTN Assessment on Current Applications of Radiomics in Oncology.

Authors:  Ke Nie; Hania Al-Hallaq; X Allen Li; Stanley H Benedict; Jason W Sohn; Jean M Moran; Yong Fan; Mi Huang; Michael V Knopp; Jeff M Michalski; James Monroe; Ceferino Obcemea; Christina I Tsien; Timothy Solberg; Jackie Wu; Ping Xia; Ying Xiao; Issam El Naqa
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-01-31       Impact factor: 7.038

9.  Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma.

Authors:  Wenbing Lv; Qingyu Yuan; Quanshi Wang; Jianhua Ma; Qianjin Feng; Wufan Chen; Arman Rahmim; Lijun Lu
Journal:  Mol Imaging Biol       Date:  2019-10       Impact factor: 3.488

10.  Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.

Authors:  Ping Yin; Ning Mao; Chao Zhao; Jiangfen Wu; Chao Sun; Lei Chen; Nan Hong
Journal:  Eur Radiol       Date:  2018-10-02       Impact factor: 5.315

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