Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Juebin Jin"'
Autor:
Xiyao Lei, Zhuo Cao, Yibo Wu, Jie Lin, Zhenhua Zhang, Juebin Jin, Yao Ai, Ji Zhang, Dexi Du, Zhifeng Tian, Congying Xie, Weiwei Yin, Xiance Jin
Publikováno v:
Insights into Imaging, Vol 14, Iss 1, Pp 1-10 (2023)
Abstract Background Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stag
Externí odkaz:
https://doaj.org/article/a8ad5c2bf1bb4643ad149e86f502f218
Autor:
Zhenhua Zhang, Xiaojie Wan, Xiyao Lei, Yibo Wu, Ji Zhang, Yao Ai, Bing Yu, Xinmiao Liu, Juebin Jin, Congying Xie, Xiance Jin
Publikováno v:
Insights into Imaging, Vol 14, Iss 1, Pp 1-10 (2023)
Abstract Background Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with feature
Externí odkaz:
https://doaj.org/article/56b5ff2738c04cbcb9b3c7367bd72f3c
Autor:
Wenliang Yu BS, Chengjian Xiao MS, Jiayi Xu MS, Juebin Jin MS, Xiance Jin PhD, Lanxiao Shen MS
Publikováno v:
Technology in Cancer Research & Treatment, Vol 22 (2023)
Purpose To predict the voxel-based dose distribution for postoperative cervical cancer patients underwent volumetric modulated arc therapy using deep learning models. Method A total of 254 patients with cervical cancer received volumetric modulated a
Externí odkaz:
https://doaj.org/article/10f7f5e9c254465a9a90cc10d98ce286
Publikováno v:
PeerJ, Vol 11, p e14546 (2023)
Background Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different model
Externí odkaz:
https://doaj.org/article/5ec4d2b721df4cafa4d6f7ec2514f27b
Autor:
Jinling Yi MS, Xiyao Lei MS, Lei Zhang BS, Qiao Zheng MS, Juebin Jin MS, Congying Xie PhD, Xiance Jin PhD, Yao Ai MS
Publikováno v:
Technology in Cancer Research & Treatment, Vol 21 (2022)
Objective To investigate the effects of different ultrasonic machines on the performance of radiomics models using ultrasound (US) images in the prediction of lymph node metastasis (LNM) for patients with cervical cancer (CC) preoperatively. Methods
Externí odkaz:
https://doaj.org/article/2f638f2536d54f60a41457168df72723
Autor:
Yinyan Teng MS, Yao Ai MS, Tao Liang BS, Bing Yu MS, Juebin Jin MS, Congying Xie PhD, Xiance Jin PhD
Publikováno v:
Technology in Cancer Research & Treatment, Vol 21 (2022)
Introduction: The purpose of this study is to investigate the effects of automatic segmentation algorithms on the performance of ultrasound (US) radiomics models in predicting the status of lymph node metastasis (LNM) for patients with early stage ce
Externí odkaz:
https://doaj.org/article/3e1074c8e8e54ad595606dcee264c06c
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
BackgroundThere is urgent need for an accurate preoperative prediction of metastatic status to optimize treatment for patients with ovarian cancer (OC). The feasibility of predicting the metastatic status based on radiomics features from preoperative
Externí odkaz:
https://doaj.org/article/588219409e5f4521bbe8e55ae69f188c
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
ObjectivesNon-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelia
Externí odkaz:
https://doaj.org/article/afc60bc79d494edf8f0378ba1e59c63b
Publikováno v:
Frontiers in Oncology, Vol 10 (2021)
Prognostic parameters and models were believed to be helpful in improving the treatment outcome for patients with brain metastasis (BM). The purpose of this study was to investigate the feasibility of computer tomography (CT) radiomics based nomogram
Externí odkaz:
https://doaj.org/article/4cb7fc6ae2654896806dd3a57e4026c7
Publikováno v:
Frontiers in Oncology, Vol 10 (2021)
Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms b
Externí odkaz:
https://doaj.org/article/fdb8266eec8549ffaa9b93e7aee40d4f