J. Gong

发表

Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment

Weijun Peng, Bin Zhao, Shengping Wang, 2020, Frontiers in Oncology.

Short-term outcomes of laparoscopic total mesorectal excision compared to open surgery.

Xinxiang Li, S. Cai, Ye Xu, 2012, World journal of gastroenterology.

A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI

Weijun Peng, Yajia Gu, J. Gong, 2021, European Radiology.

Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients

J. Gong, G. Cai, T. Tong, 2022, Journal of Hematology & Oncology.

Pretreatment Multiparametric MRI‐Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors

Weijun Peng, Yajia Gu, J. Gong, 2022, Journal of magnetic resonance imaging : JMRI.

Special issue “The advance of solid tumor research in China”: Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep‐learning features of primary lesions and peripheral lymph nodes

Dan Huang, J. Gong, T. Tong, 2022, International journal of cancer.

Multiparametric MR Imaging Radiomics Signatures for Assessing the Recurrence Risk of ER+/HER2− Breast Cancer Quantified With 21‐Gene Recurrence Score

W. Liu, Weijun Peng, Yang Chen, 2022, Journal of magnetic resonance imaging : JMRI.

A CT-Based Radiomics Approach to Predict Nivolumab Response in Advanced Non-Small-Cell Lung Cancer

QUAN LIU, Hui Yu, Jialei Wang, 2021, Frontiers in Oncology.

Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan

Jing Gong, Wen Hao, Xianwu Xia, 2020, Frontiers in Oncology.

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis

J. Gong, T. Tong, F. Feng, 2022, Frontiers in Oncology.

A “Seed-and-Soil” Radiomics Model Predicts Brain Metastasis Development in Lung Cancer: Implications for Risk-Stratified Prophylactic Cranial Irradiation

Yajia Gu, J. Gong, Zhengfei Zhu, 2023, Cancers.

A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer

Ji-yu Liu, Weijun Peng, Yajia Gu, 2022, Oncoimmunology.

Prognosis Prediction for Stage II Colorectal Cancer by Fusing CT Radiomics and Deep Learning Features of Primary Lesion and Peripheral Lymph Nodes

Dan Huang, J. Gong, T. Tong, 2020, SSRN Electronic Journal.

Multiparametric MRI-based radiomics signature for preoperative estimation of tumor-stroma ratio in rectal cancer

Dan Huang, J. Gong, C. Fu, 2020, European Radiology.

Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study

Ji-yu Liu, Weijun Peng, Shengping Wang, 2021, Cancers.

Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features

Weijun Peng, Shengping Wang, Yajia Gu, 2021, Physics in medicine and biology.

Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram

Zaiyi Liu, Yajia Gu, J. Gong, 2023, European Radiology.

Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma.

W. Peng, Yajia Gu, J. Gong, 2021, AJR. American journal of roentgenology.

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 medicine

Yiqun Sun, Zhen Zhang, J. Gong, 2022, EPMA Journal.

Applying multisequence MRI radiomics of the primary tumor and lymph node to predict HPV-related p16 status in patients with oropharyngeal squamous cell carcinoma

Chaosu Hu, J. Gong, Tingting Xu, 2023, Quantitative imaging in medicine and surgery.