Zobrazeno 1 - 10
of 12
pro vyhledávání: '"TingDan Hu"'
Autor:
TingDan Hu, Jing Gong, YiQun Sun, MengLei Li, ChongPeng Cai, XinXiang Li, YanFen Cui, XiaoYan Zhang, Tong Tong
Publikováno v:
MedComm, Vol 5, Iss 7, Pp n/a-n/a (2024)
Abstract Our study investigated whether magnetic resonance imaging (MRI)‐based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC).
Externí odkaz:
https://doaj.org/article/e21fff32b91f44f6b42027bda0859ca2
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:
Renjie Wang, Weixing Dai, Jing Gong, Mingzhu Huang, Tingdan Hu, Hang Li, Kailin Lin, Cong Tan, Hong Hu, Tong Tong, Guoxiang Cai
Publikováno v:
Journal of Hematology & Oncology, Vol 15, Iss 1, Pp 1-6 (2022)
Abstract Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics featu
Externí odkaz:
https://doaj.org/article/29fc146c7b254c4dba1ead83145713ea
Autor:
TingDan Hu, ShengPing Wang, Xiangyu E, Ye Yuan, Lv Huang, JiaZhou Wang, DeBing Shi, Yuan Li, WeiJun Peng, Tong Tong
Publikováno v:
Frontiers in Oncology, Vol 9 (2019)
Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer (CRC) and compare the efficacy of single-slice and whole-lesion histog
Externí odkaz:
https://doaj.org/article/9a1e2a0d50b64adf895977236bab2590
Autor:
Jing Gong, Jiyu Liu, Haiming Li, Hui Zhu, Tingting Wang, Tingdan Hu, Menglei Li, Xianwu Xia, Xianfang Hu, Weijun Peng, Shengping Wang, Tong Tong, Yajia Gu
Publikováno v:
Cancers, Vol 13, Iss 13, p 3300 (2021)
This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospecti
Externí odkaz:
https://doaj.org/article/07e96e24d94f4605869cc916cda9bfa0
Autor:
TingDan Hu, Jing Gong, Yanfen Cui, XiaoYan Zhang, Menglei Li, ChongPeng Cai, Ying-Shi Sun, YiQun Sun, Tong Tong
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
Cancer Management and Research. 11:10445-10453
Purpose The objective of this research was to validate the diagnostic value of three-dimensional texture parameters and clinical characteristics in the differentiation of colorectal signet-ring cell carcinoma (SRCC) and adenocarcinoma (AC). Methods W
Autor:
Renjie Wang, Weixing Dai, Jing Gong, Mingzhu Huang, Tingdan Hu, Hang Li, Kailin Lin, Cong Tan, Hong Hu, Tong Tong, Guoxiang Cai
Publikováno v:
Journal of Hematology & Oncology
Journal of Hematology & Oncology, Vol 15, Iss 1, Pp 1-6 (2022)
Journal of Hematology & Oncology, Vol 15, Iss 1, Pp 1-6 (2022)
Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features, Immu
Autor:
Weijun Peng, Hui Zhu, Jiyu Liu, Yajia Gu, Xianwu Xia, Shengping Wang, Tong Tong, Xianfang Hu, Haiming Li, Tingdan Hu, Tingting Wang, Jing Gong, Menglei Li
Publikováno v:
Cancers, Vol 13, Iss 3300, p 3300 (2021)
Cancers
Volume 13
Issue 13
Cancers
Volume 13
Issue 13
This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospecti
Publikováno v:
European radiology. 31(5)
To determine whether a radiomics signature (rad-score) outperforms ADC in TSR estimation by developing a radiomics biomarker for preoperative TSR diagnosis in rectal cancer. This study included 149 patients (119 and 30 in the training and validation