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Guodong Jing

Showing results (1-10 of 9) with videos related to

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Frontiers in Medicine|September 2, 2025
Comparison between day surgery and non-day surgery in the procedure for prolapse and hemorrhoids (grades III-IV) with MRI-assisted diagnosis: a retrospective cohort studyShaohua Zhang, Yanbin Zhao, Yifan Wei, et al.
Frontiers in Medicine|June 29, 2026
Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflectionShaoting Zhang, Fangying Chen, Minglu Liu, et al.
Frontiers in Oncology|August 1, 2022
Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogramGuodong Jing, Pengyi Xing, Zhihui Li, et al.
Frontiers in Surgery|April 25, 2025
Enhanced precision in prostate surgery: determining key factors for rectal positive surgical margins through integrated imaging and clinical data analysisYufan Wu, Fei Liu, Shiyu Ma, et al.
Biomed Research International|August 29, 2022
Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary StudyGuodong Jing, Yukun Chen, Xiaolu Ma, et al.
Academic Radiology|September 30, 2022
A CT-Based Radiomics Model for Evaluating Peritoneal Cancer Index in Peritoneal Metastasis Cases: A Preliminary StudyQianwen Zhang, Yuan Yuan, Sijie Li, et al.
Frontiers in Medicine|December 18, 2023
Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center studyShiyu Ma, Haidi Lu, Guodong Jing, et al.
The EPMA Journal|December 12, 2022
Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicineZonglin Liu, Yueming Wang, Fu Shen, et al.
Frontiers in Oncology|November 25, 2021
CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal AdenocarcinomaYinghao Meng, Hao Zhang, Qi Li, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Frontiers in Medicine|September 2, 2025
Comparison between day surgery and non-day surgery in the procedure for prolapse and hemorrhoids (grades III-IV) with MRI-assisted diagnosis: a retrospective cohort studyShaohua Zhang, Yanbin Zhao, Yifan Wei, et al.
Frontiers in Medicine|June 29, 2026
Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflectionShaoting Zhang, Fangying Chen, Minglu Liu, et al.
Frontiers in Oncology|August 1, 2022
Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogramGuodong Jing, Pengyi Xing, Zhihui Li, et al.
Frontiers in Surgery|April 25, 2025
Enhanced precision in prostate surgery: determining key factors for rectal positive surgical margins through integrated imaging and clinical data analysisYufan Wu, Fei Liu, Shiyu Ma, et al.
Biomed Research International|August 29, 2022
Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary StudyGuodong Jing, Yukun Chen, Xiaolu Ma, et al.
Academic Radiology|September 30, 2022
A CT-Based Radiomics Model for Evaluating Peritoneal Cancer Index in Peritoneal Metastasis Cases: A Preliminary StudyQianwen Zhang, Yuan Yuan, Sijie Li, et al.
Frontiers in Medicine|December 18, 2023
Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center studyShiyu Ma, Haidi Lu, Guodong Jing, et al.
The EPMA Journal|December 12, 2022
Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicineZonglin Liu, Yueming Wang, Fu Shen, et al.
Frontiers in Oncology|November 25, 2021
CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal AdenocarcinomaYinghao Meng, Hao Zhang, Qi Li, et al.
Pageof 1