个人科研网厅 | | 帮助
高级检索
当前位置： 首页 > 详情页

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging

| 认领 | 导出 | |

文献详情

资源类型：
WOS体系：
Pubmed体系：

收录情况： ◇ SCIE

作者：
Zhang YuChen[1,2] * ; Deng YiShu[1,3,4] * ; Zou QiHua[1,2] * ; Jing BingZhong[1,3] * ; Cai PeiQiang[1,5] * ; Tian XiaoPeng[1,2] * ; Yang Yu[6] ; Li BingZong[7] ; Liu Fang[8] ; Li ZhiHua[9] ; Liu ZaiYi[10,11] ; Feng ShiTing[12] ; Peng TingSheng[13] ; Dong YuJun[14] ; Wang XinYan[15] ; Ruan GuangYing[1,5] ; He Yun[1,5] ; Cui ChunYan[1,5] ; Li Jiao[1,5] ; Luo Xiao[1,5] ; Huang HuiQiang[1,2] ; Chen HaoHua[1,3] ; Li SongQi[16] ; Sun Ying[1,17] ; Xie ChuanMiao[1,5] ; Wang Liang[18] ; Li ChaoFeng[1,3] ; Cai QingQing[1,2] ;
机构： [1]State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China [2]Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China [3]Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China [4]School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, P.R. China [5]Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China [6]Department of Lymphadenoma and Head & Neck Medical Oncology, Fujian Provincial Cancer Hospital & Institute, Fuzhou, P.R. China [7]Department of Hematology, The Second Affiliated Hospital of Suzhou University, Jiangsu, P.R. China [8]Department of Pathology, The First People’s Hospital of Foshan, Foshan, P.R. China [9]Department of Oncology, Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, P.R. China [10]Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, P.R. China [11]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, P.R. China [12]Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, P.R. China [13]Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, P.R. China [14]Department of Hematology, Peking University First Hospital, Beijing 100034, P.R. China [15]Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, P.R. China [16]Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, P.R. China [17]Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China [18]Department of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, P.R. China
出处：
DOI： 10.1016/j.xcrm.2024.101551
ISSN：

摘要：
Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.905-0.960 in detecting malignant nasopharyngeal lesions and distinguishing NKTCL from nasopharyngeal carcinoma in independent validation datasets. In comparison to human radiologists, the diagnostic systems show higher accuracies than resident radiologists and comparable ones to senior radiologists. The prognostic system shows promising performance in predicting survival outcomes of NKTCL and outperforms several clinical models. For patients with early-stage NKTCL, only the high-risk group benefits from early radiotherapy (hazard ratio = 0.414 vs. late radiotherapy; 95% confidence interval, 0.190-0.900, p = 0.022), while progression-free survival does not differ in the low-risk group. In conclusion, AI-based systems show potential in assisting accurate diagnosis and prognosis prediction and may contribute to therapeutic optimization for NKTCL.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

基金：
This work was supported by grants from the National Key Research and Development Program (2022YFC2502602), the National Natural Science Foundation of China (82230001 and 82270199), the Guangzhou Science and Technology Program (2024B03J1291), the Sun Yat-Sen University Clinical Research 5010 Program (2020009), and the Beijing Xisike Clinical Oncology Research Foundation (Y-XD2019-124 and Y-SY2021ZD-0110).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类 | 1 区 医学
小类 | 1 区 医学：研究与实验 2 区 细胞生物学
最新[2023]版：
大类 | 1 区 医学
小类 | 1 区 医学：研究与实验 2 区 细胞生物学
JCR分区：
出版当年[2022]版：
Q1 CELL BIOLOGY Q1 MEDICINE, RESEARCH & EXPERIMENTAL
最新[2023]版：
Q1 MEDICINE, RESEARCH & EXPERIMENTAL Q1 CELL BIOLOGY

影响因子： 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

第一作者：
第一作者机构： [1]State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China [2]Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China
共同第一作者：
通讯作者：
通讯机构： [1]State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China [2]Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China [3]Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China [5]Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China
推荐引用方式(GB/T 7714)：
Zhang YuChen,Deng YiShu,Zou QiHua,et al.Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging[J].CELL REPORTS MEDICINE.2024,5(5):101551.doi:10.1016/j.xcrm.2024.101551.
APA：
Zhang YuChen,Deng YiShu,Zou QiHua,Jing BingZhong,Cai PeiQiang...&Cai QingQing.(2024).Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.CELL REPORTS MEDICINE,5,(5)
MLA：
Zhang YuChen,et al."Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging".CELL REPORTS MEDICINE 5..5(2024):101551

资源点击量：21166 今日访问量：0 总访问量：1219 更新日期：2025-01-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 首都医科大学附属北京同仁医院 技术支持：重庆聚合科技有限公司 地址：北京市东城区东交民巷1号(100730)