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Rongzhen Zhou

Showing results (1-10 of 7) with videos related to

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Abdominal Radiology (New York)|July 27, 2022
The maximum length of T2-dark intraplacental bands may help predict intraoperative haemorrhage in pregnant women with placenta accreta spectrum (PAS)Xiuli Wu, Rongzhen Zhou, Minjie Lin, et al.
Frontiers in Oncology|June 4, 2021
Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell CarcinomaWenbing Ji, Jian Wang, Rongzhen Zhou, et al.
Journal of Cardiovascular Pharmacology|December 31, 2024
Predicting Vulnerability Status of Carotid Plaques Using CTA-Based Quantitative AnalysisQun Lao, Rongzhen Zhou, Yitian Wu, et al.
The British Journal of Radiology|October 9, 2025
Non-invasive prediction of Central lymph node metastasis in papillary thyroid microcarcinoma with machine learning-based CT radiomics: a multicenter studyFeng Cheng, Guihan Lin, Weiyue Chen, et al.
Abdominal Radiology (New York)|March 13, 2021
MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cmYani Kuang, Renzhan Li, Peng Jia, et al.
Patient Education and Counseling|February 10, 2024
Using teach-back in patient education to improve patient satisfaction and the clarity of magnetic resonance imagingYingying Jiang, Yitian Wu, Qilong Deng, et al.
Academic Radiology|March 14, 2026
A Multicenter Study on Deep Learning Model-Assisted Detection of Brain Metastases in MR ImagesMeiqi Hua, Liyong Zhuo, Yu Zhang, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Abdominal Radiology (New York)|July 27, 2022
The maximum length of T2-dark intraplacental bands may help predict intraoperative haemorrhage in pregnant women with placenta accreta spectrum (PAS)Xiuli Wu, Rongzhen Zhou, Minjie Lin, et al.
Frontiers in Oncology|June 4, 2021
Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell CarcinomaWenbing Ji, Jian Wang, Rongzhen Zhou, et al.
Journal of Cardiovascular Pharmacology|December 31, 2024
Predicting Vulnerability Status of Carotid Plaques Using CTA-Based Quantitative AnalysisQun Lao, Rongzhen Zhou, Yitian Wu, et al.
The British Journal of Radiology|October 9, 2025
Non-invasive prediction of Central lymph node metastasis in papillary thyroid microcarcinoma with machine learning-based CT radiomics: a multicenter studyFeng Cheng, Guihan Lin, Weiyue Chen, et al.
Abdominal Radiology (New York)|March 13, 2021
MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cmYani Kuang, Renzhan Li, Peng Jia, et al.
Patient Education and Counseling|February 10, 2024
Using teach-back in patient education to improve patient satisfaction and the clarity of magnetic resonance imagingYingying Jiang, Yitian Wu, Qilong Deng, et al.
Academic Radiology|March 14, 2026
A Multicenter Study on Deep Learning Model-Assisted Detection of Brain Metastases in MR ImagesMeiqi Hua, Liyong Zhuo, Yu Zhang, et al.
Pageof 1