Lingyun Wang

发表

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

L. Zou, Huan Zhang, J. Ji, 2020, Journal of Cancer.

Motion artifact removal in coronary CT angiography based on generative adversarial networks

Xueqian Xie, Yaping Zhang, B. Jiang, 2022, European Radiology.

Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study

M. Wels, Yong Chen, Jingwen Tan, 2022, Acta radiologica.

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma

R. Vliegenthart, Xueqian Xie, G. D. de Bock, 2021, Frontiers in Oncology.

Euclidean distance-based Raman spectroscopy (EDRS) for the prognosis analysis of gastric cancer: A solution to tumor heterogeneity.

Jian Ye, Huan Zhang, Lan Zhu, 2022, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

Computed Diffusion‐Weighted Images of Rectal Cancer: Image Quality, Restaging, and Treatment Response after Neoadjuvant Therapy

C. Fu, Huan Zhang, L. Du, 2023, Journal of magnetic resonance imaging : JMRI.

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images

Fuhua Yan, W. Yao, Jun Zhang, 2021, Frontiers in Oncology.

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

M. Wels, W. Yao, Huan Zhang, 2021, Frontiers in Oncology.

Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy.

M. Wels, W. Yao, E. Li, 2021, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Xueqian Xie, Yaping Zhang, B. Jiang, 2021, European Journal of Hybrid Imaging.

Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT.

Xueqian Xie, Yaping Zhang, B. Jiang, 2022, The British journal of radiology.

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability

Huan Zhang, Jianying Li, Z. Pan, 2023, Insights into Imaging.

Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels

Huan Zhang, Yong Chen, Lan Wang, 2024, Journal of imaging informatics in medicine.

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers

Yangfan Hu, Huan Zhang, L. Du, 2023, European Radiology.

Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification

Yaping Zhang, Lingyun Wang, Lu Zhang, 2024, European radiology.

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study.

Yangfan Hu, Yue Xing, Lingyun Wang, 2024, European radiology.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

Yangfan Hu, Yue Xing, Lingyun Wang, 2024, Acta radiologica.

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

Yangfan Hu, Yue Xing, Lingyun Wang, 2024, BMC Medical Imaging.