Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Yajia Gu"'
Publikováno v:
Cancer Imaging, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract Background Triple-negative breast cancer (TNBC) is highly heterogeneous, resulting in different responses to neoadjuvant chemotherapy (NAC) and prognoses among patients. This study sought to characterize the heterogeneity of TNBC on MRI and
Externí odkaz:
https://doaj.org/article/523ed13c920f4affab5ef527bbc128fe
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Objectives To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT. Materials and methods This study prospectively enrolled patients with susp
Externí odkaz:
https://doaj.org/article/bd6d55347d6c46daa8a13cf473072d96
Autor:
Ming Fan, Kailang Wang, Da Pan, Xuan Cao, Zhihao Li, Songlin He, Sangma Xie, Chao You, Yajia Gu, Lihua Li
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-15 (2024)
Abstract Background Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed
Externí odkaz:
https://doaj.org/article/db36ec96d1cc48a28da718a564632fa2
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative b
Externí odkaz:
https://doaj.org/article/816cd78bac324571bb7ea8b3ffa18d9d
Publikováno v:
Frontiers in Endocrinology, Vol 15 (2024)
ObjectivesThe growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung
Externí odkaz:
https://doaj.org/article/a2b9f97897ff4c68adcedfe7d8726617
Autor:
Jing Gong, Ting Wang, Zezhou Wang, Xiao Chu, Tingdan Hu, Menglei Li, Weijun Peng, Feng Feng, Tong Tong, Yajia Gu
Publikováno v:
Cancer Imaging, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and comp
Externí odkaz:
https://doaj.org/article/34406188dbe54f838421ea35af6e5abf
Autor:
Luyi Lin, Haiming Li, Xin Wang, Zezhou Wang, Guanhua Su, Jiayin Zhou, Shiyun Sun, Xiaowen Ma, Yan Chen, Chao You, Yajia Gu
Publikováno v:
Cancer Medicine, Vol 12, Iss 24, Pp 21639-21650 (2023)
Abstract Background and Aim The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple‐negative breast cancer (TNBC) to immunomodulators. The potential of mul
Externí odkaz:
https://doaj.org/article/052bd368f303498da0a8d143c5124e5f
Autor:
Wenchao Gu, Yingli Chen, Haibin Zhu, Haidi Chen, Zongcheng Yang, Shaocong Mo, Hongyue Zhao, Lei Chen, Takahito Nakajima, XianJun Yu, Shunrong Ji, YaJia Gu, Jie Chen, Wei Tang
Publikováno v:
EClinicalMedicine, Vol 65, Iss , Pp 102269- (2023)
Summary: Background: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combina
Externí odkaz:
https://doaj.org/article/e376322eb6614260b0957ace248cdc1c
Autor:
Chao You, Yiyuan Shen, Shiyun Sun, Jiayin Zhou, Jiawei Li, Guanhua Su, Eleni Michalopoulou, Weijun Peng, Yajia Gu, Weisheng Guo, Heqi Cao
Publikováno v:
Exploration, Vol 3, Iss 5, Pp n/a-n/a (2023)
Abstract Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer‐related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand
Externí odkaz:
https://doaj.org/article/6db044e5263f4adba957e7b939474a42
Autor:
Tiantian Zheng, Fan Lin, Xianglin Li, Tongpeng Chu, Jing Gao, Shijie Zhang, Ziyin Li, Yajia Gu, Simin Wang, Feng Zhao, Heng Ma, Haizhu Xie, Cong Xu, Haicheng Zhang, Ning Mao
Publikováno v:
EClinicalMedicine, Vol 58, Iss , Pp 101913- (2023)
Summary: Background: Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial i
Externí odkaz:
https://doaj.org/article/37bc6cabfb24424da1b8d9b934ea805b