Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Xuelei Ma"'
Publikováno v:
Journal of Magnetic Resonance Imaging. 56:1733-1745
MRI acts as a potential resource for exploration and interpretation to identify tumor characterization by advanced computer-aided diagnostic (CAD) methods.To evaluate and validate the performance of MRI-based CAD models for identifying low-grade and
Publikováno v:
Strahlentherapie und Onkologie. 198:183-193
Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the predictive performance of the computed tomography (CT)-based model using deep learning features for identification
Publikováno v:
Journal of Ultrasound in Medicine. 41:1537-1547
OBJECTIVES The aim of this study is to systematically evaluate the diagnostic ability of the contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) in hepatocellular carcinoma (HCC). METHODS We searched relevant studies
Publikováno v:
Thoracic Cancer
Thoracic Cancer, Vol 12, Iss 21, Pp 2914-2923 (2021)
Thoracic Cancer, Vol 12, Iss 21, Pp 2914-2923 (2021)
Background Inflammation‐nutritional markers of peripheral blood are easily assessed and can predict survival. The aim of this study was to investigate the association between inflammation‐nutritional parameters and survival of anti‐programmed d
Autor:
Yunuo Zhao, Xuelei Ma, Tao Zhang, Zheyu Chen, Hanyue Xu, Hang Wang, Youyin Tang, Yichun Liu, Xianghong Zhou
Publikováno v:
World Journal of Surgical Oncology, Vol 19, Iss 1, Pp 1-13 (2021)
World Journal of Surgical Oncology
World Journal of Surgical Oncology
Background Intrahepatic cholangiocarcinoma is an aggressive liver carcinoma with increasing incidence and mortality. A good auxiliary prognostic prediction tool is desperately needed for the development of treatment strategies. The purpose of this st
Publikováno v:
Nutrition and cancer. 74(10)
Publikováno v:
International Journal of Medical Sciences
Objective: This study aimed to establish and validate a radiomics nomogram comprised of clinical factors and radiomics signatures to predict prognosis of primary hepatic sarcomatoid carcinoma (PHSC) patients after surgical resection. Methods: In this
Publikováno v:
Orphanet journal of rare diseases. 17(1)
BackgroundDeep learning methods have great potential to predict tumor characterization, such as histological diagnosis and genetic aberration. The objective of this study was to evaluate and validate the predictive performance of multimodality imagin
Publikováno v:
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. 48(5)
Objective To evaluate the performance of a deep learning (DL)-based radiomics strategy on contrast-enhanced computed tomography (CT) to predict microvascular invasion (MVI) status and clinical outcomes, recurrence-free survival (RFS) and overall surv
Autor:
Wenting Xu, Hui Xu, Shihao Duan, Xuelei Ma, Chuyu Shen, Linyan Chen, Xuejin Ou, Hanxiao Chen, Sha Zhu, Yingjie Wang
Publikováno v:
The Quarterly Journal of Nuclear Medicine and Molecular Imaging. 65
BACKGROUND The aim of this study is to determine the differential diagnostic value of texture parameters of PET/CT on renal cell carcinoma and renal lymphoma. METHODS Twenty renal lymphoma and 18 renal cell carcinoma (RCC) patients were analyzed in t