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Your search keyword '"Liu, Zhou"' showing total 6 results
Start Over Author "Liu, Zhou" Journal european radiology
6 results on '"Liu, Zhou"'

5. Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions

Author

Liu, Zhou, primary, Yao, Bingyu, additional, Wen, Jie, additional, Wang, Meng, additional, Ren, Ya, additional, Chen, Yuming, additional, Hu, Zhanli, additional, Li, Ye, additional, Liang, Dong, additional, Liu, Xin, additional, Zheng, Hairong, additional, Luo, Dehong, additional, and Zhang, Na, additional

Published
2023
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6. CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy

Author

Haifeng Wei, Liu Zhou, Jiayi Zhang, Xin Gao, Xingyu Zhao, Wei Xia, Fengchang Yang, Junming Jian, Rui Zhang, Shuanghu Yuan, and Jinxue Feng

Subjects
medicine.medical_specialty, business.industry, medicine.medical_treatment, Retrospective cohort study, General Medicine, medicine.disease, Tumor response, Targeted therapy, Radiomics, medicine, Radiology, Nuclear Medicine and imaging, Radiology, Non small cell, First line chemotherapy, Lung cancer, business, Neuroradiology
Abstract

The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient’s CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value

Published
2021

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