Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Jia-Liang Ren"'
Autor:
Xu Jiang, Chao Luo, Xin Peng, Jing Zhang, Lin Yang, Li-Zhi Liu, Yan-Fen Cui, Meng-Wen Liu, Lei Miao, Jiu-Ming Jiang, Jia-Liang Ren, Xiao-Tang Yang, Meng Li, Li Zhang
Publikováno v:
Respiratory Research, Vol 25, Iss 1, Pp 1-12 (2024)
Abstract Background This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 − 2N0M0 (cT1 − 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intr
Externí odkaz:
https://doaj.org/article/94b852cfb08441a294cbb98b658ac520
Publikováno v:
European Journal of Radiology Open, Vol 13, Iss , Pp 100592- (2024)
Background: Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC). Objective: This study aimed to explore the effectiveness of a multis
Externí odkaz:
https://doaj.org/article/c30aa443a4494f30b013b22312369baf
Publikováno v:
Diagnostic and Interventional Radiology, Vol 28, Iss 6, Pp 532-539 (2022)
PURPOSEThe stomach is the most common site of gastrointestinal stromal tumors (GISTs). In this study, clinical model, radiomics models, and nomogram were constructed to compare and assess the clinical value of each model in predicting the preoperativ
Externí odkaz:
https://doaj.org/article/4f752f0da9d0401fa2ce6e74db8b9a67
Publikováno v:
European Journal of Radiology Open, Vol 10, Iss , Pp 100476- (2023)
Purpose: To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological c
Externí odkaz:
https://doaj.org/article/de70da6739bf4e2b8b893cee6bc99ee9
Autor:
Tao Yuan, Zhen Gao, Fei Wang, Jia-Liang Ren, Tianda Wang, Hongbo Zhong, Guodong Gao, Guanmin Quan
Publikováno v:
Frontiers in Oncology, Vol 12 (2022)
AimsTo investigate whether the relative signal intensity surrounding the residual cavity on T2-fluid-attenuated inversion recovery (rFLAIR) can improve the survival prediction of lower-grade glioma (LGG) patients.MethodsClinical and pathological data
Externí odkaz:
https://doaj.org/article/513a5a690b624e6cb011a8fb0c18339c
Autor:
Ping Wang, Xu Pei, Xiao-Ping Yin, Jia-Liang Ren, Yun Wang, Lu-Yao Ma, Xiao-Guang Du, Bu-Lang Gao
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Abstract This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal c
Externí odkaz:
https://doaj.org/article/acf1a865aae440a3b0aee58e4b605312
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
At present, it is still challenging to predict the clinical outcome of acute ischemic stroke (AIS). In this retrospective study, we explored whether radiomics features extracted from fluid-attenuated inversion recovery (FLAIR) and apparent diffusion
Externí odkaz:
https://doaj.org/article/b351d12d55ec4c98a423083ca9ad0a89
Autor:
Chen-Xi Liu, Li-Jun Heng, Yu Han, Sheng-Zhong Wang, Lin-Feng Yan, Ying Yu, Jia-Liang Ren, Wen Wang, Yu-Chuan Hu, Guang-Bin Cui
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
ObjectiveTo explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA).MethodsForty-nine patients with GH-secreting PA confirmed by t
Externí odkaz:
https://doaj.org/article/f3a8bfa9ab9049048e5edae10c4ebc09
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
PurposeThis study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas.Materials and MethodsCT data of 190 cases with pathologically c
Externí odkaz:
https://doaj.org/article/238fb3787222429eaf3a4c0f4637326a
Publikováno v:
La radiologia medica. 128:242-251