MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer

doi:10.1016/j.radonc.2024.110328

科研成果详情

题名	MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer
作者	Ai, Yao1; Zhu, Xiaoyang 2; Zhang, Yu 3; Li, Wenlong 1; Li, Heng1; Zhao, Zeshuo1; Zhang, Jicheng1; Ning, Boda1; Li, Chenyu1; Zheng, Qiao 1; Zhang, Ji1; Jin, Juebin4; Li, Yiran 1; Xie, Congying1; Jin, Xiance1,5
发表日期	2024-05-16
发表期刊	Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 影响因子和分区
语种	英语
原始文献类型	Journal Article
关键词	Early-stage cervical cancer Machine learning Nomogram Radiomics Recurrence
其他关键词	LYMPH-NODE METASTASIS ; THERAPY RESPONSE ; HETEROGENEITY
摘要	Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC., 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments., A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC., In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.
资助项目	Wenzhou Municipal Science and Technology Bureau[ZY2022016,ZY2020011,Y20220445,Y2023798];
出版者	Elsevier Ireland Ltd
ISSN	0167-8140
EISSN	1879-0887
卷号	197
DOI	10.1016/j.radonc.2024.110328
页数	8
WOS类目	Oncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Oncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001289732000001
收录类别	PUBMED ; SCOPUS ; SCIE
URL	查看原文
PubMed ID	38761884
SCOPUSEID	2-s2.0-85193494989
通讯作者地址	[Xie, Congying]Radiotherapy Center,the First Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China
Scopus学科分类	Hematology;Oncology;Radiology, Nuclear Medicine and Imaging
TOP期刊	TOP期刊
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/212911
专题	附属第一医院基础医学院（机能实验教学中心）
通讯作者	Xie, Congying; Jin, Xiance
作者单位	1.Radiotherapy Center,the First Affiliated Hospital of Wenzhou Medical University,Wenzhou,China; 2.Department of Radiotherapy,the Second Affiliated Hospital Zhejiang University School of Medicine,Zhejiang,China; 3.Department of Information Division,the First Affiliated Hospital of Wenzhou Medical University,Wenzhou,China; 4.Department of Medical Engineering,the First Affiliated Hospital of Wenzhou Medical University,Wenzhou,China; 5.School of Basic Medical Science,Wenzhou Medical University,Wenzhou,China
第一作者单位	附属第一医院
通讯作者单位	附属第一医院
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	Ai, Yao,Zhu, Xiaoyang,Zhang, Yu,et al. MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer[J]. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology,2024,197.
APA	Ai, Yao., Zhu, Xiaoyang., Zhang, Yu., Li, Wenlong., Li, Heng., ... & Jin, Xiance. (2024). MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 197.
MLA	Ai, Yao,et al."MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer".Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 197(2024).