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Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer

文献类型：期刊论文

作者Fang, Mengjie1,5; Kan, Yangyang3,4; Dong, Di1,5; Yu, Tao3,4; Zhao, Nannan3,4; Jiang, Wenyan3,4; Zhong, Lianzhen1,5; Hu, Chaoen1,5; Luo, Yahong3,4; Tian, Jie1,2
刊名FRONTIERS IN ONCOLOGY
出版日期2020-05-05
卷号10页码:8
关键词cervical cancer MRI radiomics treatment response prediction concurrent chemotherapy and radiation therapy precision medicine
ISSN号2234-943X
DOI10.3389/fonc.2020.00563
通讯作者Luo, Yahong(luoyahong8888@hotmail.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Objectives: To develop a radiomic model based on multiparametric magnetic resonance imaging (MRI) for predicting treatment response prior to commencing concurrent chemotherapy and radiation therapy (CCRT) for locally advanced cervical cancer. Materials and methods: The retrospective study enrolled 120 patients (allocated to a training or a test set) with locally advanced cervical cancer who underwent CCRT between December 2014 and June 2017. All patients enrolled underwent MRI with nine sequences before treatment and again at the end of the fourth week of treatment. Responses were evaluated by MRI according to RECIST standards, and patients were divided into a responder group or non-responder group. For every MRI sequence, a total of 114 radiomic features were extracted from the outlined tumor habitat. On the training set, the least absolute shrinkage and selection operator method was used to select key features and to construct nine habitat signatures. Then, three kinds of machine learning models were compared and applied to integrate these predictive signatures and the clinical characteristics into a radiomic model. The discrimination ability, reliability, and calibration of our radiomic model were evaluated. Results: The radiomic model, which consisted of three habitat signatures from sagittal T2 image, axial T1 enhanced-MRI image, and ADC image, respectively, has shown good predictive performance, with area under the curve of 0.820 (95% CI: 0.713-0.927) in the training set and 0.798 (95% CI: 0.678-0.917) in the test set. Meanwhile, the model proved to perform better than each single signature or clinical characteristic. Conclusions: A radiomic model employing features from multiple tumor habitats held the ability for predicting treatment response in patients with locally advanced cervical cancer before commencing CCRT. These results illustrated a potential new tool for improving medical decision-making and therapeutic strategies.
WOS关键词HETEROGENEITY ; IMAGES ; CHINA ; MRI
资助项目National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向Oncology
语种英语
WOS记录号WOS:000536333600001
出版者FRONTIERS MEDIA SA
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/39538]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Luo, Yahong; Tian, Jie
作者单位1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
2.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
3.Liaoning Canc Hosp & Inst, Shenyang, Peoples R China
4.China Med Univ, Canc Hosp, Shenyang, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Fang, Mengjie,Kan, Yangyang,Dong, Di,et al. Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer[J]. FRONTIERS IN ONCOLOGY,2020,10:8.
APA Fang, Mengjie.,Kan, Yangyang.,Dong, Di.,Yu, Tao.,Zhao, Nannan.,...&Tian, Jie.(2020).Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.FRONTIERS IN ONCOLOGY,10,8.
MLA Fang, Mengjie,et al."Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer".FRONTIERS IN ONCOLOGY 10(2020):8.

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来源：自动化研究所

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