8 Papers
6 Citations
Ting Wang is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Medicine & Radiogenomics. The author has an hindex of 2, co-authored 3 publications.
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Papers
Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study
Zi-Jing Lin,Ting Wang,Qiong Li,Qiu Bi,Yaoxin Wang,Yingwei Luo,Feng Feng,Mei Ling Xiao,Yajia Gu,Jin Jian Qiang,Haiming Li +10 more
TL;DR: The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model, and could be a valuable predictor for the recurrence risk of EC patients.
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Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer
Zi-Jing Lin,Ting Wang,Haiming Li,Mei Ling Xiao,Xiaoliang Ma,Yajia Gu,Jin Jian Qiang +6 more
TL;DR: In this article , a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in endometrial cancer (EC) patients was developed and evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA).
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Patent
Method for analyzing correlation between CT imaging genomics characteristics and gene expression in lung cancer
Ting Wang,Jing Gong,Shengdong Nie +2 more
- 27 Nov 2018
TL;DR: In this article, a semi-automatic segmentation method was used to extract CT imaging genomics characteristics of a segmented tumor were; performing clustering analysis on the basis of preprocessed gene data, and taking a first principal component as a representative of a gene clustering result with a similar expression profile.
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Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.
Jing Gong,Fangqiu Fu,Xiaowen Ma,Ting Wang,Xiangyi Ma,Chao You,Yang Zhang,Weijun Peng,Haiquan Chen,Yajia Gu +9 more
TL;DR: The results demonstrate that the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-taskDNN models, and thefeature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSclC prognosis.
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Association of CT-based imaging features and genomic data in non-small cell lung cancer
Ting Wang,Jing Gong,Hui-Hong Duan,Lijia Wang,Shengdong Nie +4 more
- 29 Oct 2018
TL;DR: Experiment show that there are 126 significant and reliable pairwise correlations which suggest that CTbased features are minable and can reflect important biological information of NSCLC patients.
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