CASIA OpenIR  > 中国科学院分子影像重点实验室
Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis
Tian, Xin1; Sun, Caixia2,3; Liu, Zhenyu2,4; Li, Weili1; Duan, Hui1; Wang, Lu1; Fan, Huijian1; Li, Mingwei1; Li, Pengfei1; Wang, Lihui3; Liu, Ping1; Tian, Jie2,4,5; Chen, Chunlin1
Source PublicationFRONTIERS IN ONCOLOGY
ISSN2234-943X
2020-02-04
Volume10Pages:10
Corresponding AuthorWang, Lihui(wlh1984@gmail.com) ; Liu, Ping(lpivy@126.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Chen, Chunlin(ccl1@smu.edu.cn)
AbstractObjective: To investigate whether pre-treatment CT-derived radiomic features could be applied for prediction of clinical response to neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC). Patients and Methods: Two hundred and seventy-seven LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. One thousand and ninety-four radiomic features were extracted from venous contrast enhanced and non-enhanced CT imaging for each patient. Five combined methods of feature selection were used to reduce dimension of features. Radiomics signature was constructed by Random Forest (RF) method in a primary cohort of 221 patients. A combined model incorporating radiomics signature with clinical factors was developed using multivariable logistic regression. Prediction performance was then tested in a validation cohort of 56 patients. Results: Radiomics signature containing pre- and post-contrast imaging features can adequately distinguish chemotherapeutic responders from non-responders in both primary and validation cohorts [AUCs: 0.773 (95% CI, 0.701-0.845) and 0.816 (95% CI, 0.690-0.942), respectively] and remain relatively stable across centers. The combined model has a better predictive performance with an AUC of 0.803 (95% CI, 0.734-0.872) in the primary set and an AUC of 0.821 (95% CI, 0.697-0.946) in the validation set, compared to radiomics signature alone. Both models showed good discrimination, calibration. Conclusion: Newly developed radiomic model provided an easy-to-use predictor of chemotherapeutic response with improved predictive ability, which might facilitate optimal treatment strategies tailored for individual LACC patients.
Keywordlocally advanced cervical cancer (LACC) radiomics neoadjuvant chemotherapy response prediction CT
DOI10.3389/fonc.2020.00077
WOS KeywordMAGNETIC-RESONANCE ; PATHOLOGICAL RESPONSE ; RADICAL HYSTERECTOMY ; CARCINOMA ; SURGERY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81772012] ; Beijing Natural Science Foundation[7182109] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2016YFA0100900] ; National Key Research and Development Plan of China[2016YFA0100902] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC00] ; National Science and Technology Pillar Program during the Twelfth Five-year Plan Period[2014BAI05B03] ; Major Program of Natural Science Foundation of Guangdong Province[2015A030311024] ; Medical Scientific Research Foundation of Guangdong Province[A2015063] ; National Natural Science Foundation of China[81772012] ; Beijing Natural Science Foundation[7182109] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2016YFA0100900] ; National Key Research and Development Plan of China[2016YFA0100902] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC00] ; National Science and Technology Pillar Program during the Twelfth Five-year Plan Period[2014BAI05B03] ; Major Program of Natural Science Foundation of Guangdong Province[2015A030311024] ; Medical Scientific Research Foundation of Guangdong Province[A2015063]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; Chinese Academy of Sciences ; National Science and Technology Pillar Program during the Twelfth Five-year Plan Period ; Major Program of Natural Science Foundation of Guangdong Province ; Medical Scientific Research Foundation of Guangdong Province
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000515550400001
PublisherFRONTIERS MEDIA SA
Citation statistics
Cited Times:25[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38513
Collection中国科学院分子影像重点实验室
Corresponding AuthorWang, Lihui; Liu, Ping; Tian, Jie; Chen, Chunlin
Affiliation1.Southern Med Univ, Nanfang Hosp, Dept Gynaecol & Obstet, Guangzhou, Peoples R China
2.Chinese Acad Sci, CAS Key Lab Mol Imaging, Inst Automat, Beijing, Peoples R China
3.Guizhou Univ, Sch Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Tian, Xin,Sun, Caixia,Liu, Zhenyu,et al. Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis[J]. FRONTIERS IN ONCOLOGY,2020,10:10.
APA Tian, Xin.,Sun, Caixia.,Liu, Zhenyu.,Li, Weili.,Duan, Hui.,...&Chen, Chunlin.(2020).Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis.FRONTIERS IN ONCOLOGY,10,10.
MLA Tian, Xin,et al."Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis".FRONTIERS IN ONCOLOGY 10(2020):10.
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