高级检索
当前位置: 首页 > 详情页

MRI-based radiomics as response predictor to radiochemotherapy for metastatic cervical lymph node in nasopharyngeal carcinoma.

| 导出 | |

文献详情

资源类型:
WOS体系:
Pubmed体系:

收录情况: ◇ SCIE

机构: [1]Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. [2]Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. [3]Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.
出处:
ISSN:

摘要:
To establish and substantiate MRI-based radiomic models to predict the treatment response of metastatic cervical lymph node to radiochemotherapy in patients with nasopharyngeal carcinoma (NPC). A total of 145 consecutive patients with NPC were enrolled including 102 in primary cohort and 43 in validation cohort. Metastatic lymph nodes were diagnosed according to radiologic criteria and treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors. A total of 2704 radiomic features were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI) for each patient, and were selected to construct radiomic signatures for CE-T1WI, T2WI, and combined CE-T1WI and T2WI, respectively. The area under curve (AUC) of receiver operating characteristic, sensitivity, specificity, and accuracy were used to estimate the performance of these radiomic models in predicting treatment response of metastatic lymph node. No significant difference of AUC was found among radiomic signatures of CE-T1WI, T2WI, and combined CE-T1WI and T2WI in the primary and validation cohorts (all p > 0.05). For combined CE-T1WI and T2WI dataset, 12 features were selected to develop the radiomic signature. The AUC, sensitivity, specificity, and accuracy were 0.927 (0.878-0.975), 0.911 (0.804-0.970), 0.826 (0.686-0.922), and 0.872 (0.792-0.930) in primary cohort, and were 0.772 (0.624-0.920), 0.792 (0.578-0.929), 0.790 (0.544-0.939), and 0.791 (0.640-0.900) in validation cohort. MRI-based radiomic models were developed to predict the treatment response of metastatic cervical lymph nodes to radiochemotherapy in patients with NPC, which might facilitate individualized therapy for metastatic lymph nodes before treatment. Predicting the response in patients with NPC before treatment may allow more individualizing therapeutic strategy and avoid unnecessary side-effects and costs. Radiomic features extracted from metastatic cervical lymph nodes showed promising application for predicting the treatment response in NPC.

基金:
语种:
被引次数:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2021]版:
大类 | 3 区 医学
小类 | 4 区 核医学
最新[2023]版:
大类 | 4 区 医学
小类 | 4 区 核医学
JCR分区:
出版当年[2021]版:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2021版] 出版当年五年平均 出版前一年[2020版] 出版后一年[2022版]

第一作者:
第一作者机构: [1]Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. [2]Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
共同第一作者:
通讯作者:
通讯机构: [1]Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. [2]Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. [*1]Department of Radiology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu 610041, Sichuan, China.
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:43370 今日访问量:0 总访问量:3120 更新日期:2024-09-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持:重庆聚合科技有限公司 地址:成都市人民南路四段55号