CASIA OpenIR
Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer
Zhou, Xuezhi1,3; Yi, Yongju2; Liu, Zhenyu3,7; Cao, Wuteng4; Lai, Bingjia5; Sun, Kai1; Li, Longfei6; Zhou, Zhiyang4; Feng, Yanqiu2; Tian, Jie1,3,7,8
Source PublicationANNALS OF SURGICAL ONCOLOGY
ISSN1068-9265
2019-06-01
Volume26Issue:6Pages:1676-1684
Corresponding AuthorFeng, Yanqiu(foree@163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractObjectiveThe aim of this study was to investigate whether pretherapeutic, multiparametric magnetic resonance imaging (MRI) radiomic features can be used for predicting non-response to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC).MethodsWe retrospectively enrolled 425 patients with LARC [allocated in a 3:1 ratio to a primary (n=318) or validation (n=107) cohort] who received neoadjuvant therapy before surgery. All patients underwent T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MRI scans before receiving neoadjuvant therapy. We extracted 2424 radiomic features from the pretherapeutic, multiparametric MR images of each patient. The Wilcoxon rank-sum test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression were successively performed for feature selection, whereupon a multiparametric MRI-based radiomic model was established by means of multivariate logistic regression analysis. This feature selection and multivariate logistic regression analysis was also performed on all single-modality MRI data to establish four single-modality radiomic models. The performance of the five radiomic models was evaluated by receiver operating characteristic (ROC) curve analysis in both cohorts.ResultsThe multiparametric, MRI-based radiomic model based on 16 features showed good predictive performance in both the primary (p<0.01) and validation (p<0.05) cohorts, and performed better than all single-modality models. The area under the ROC curve of this multiparametric MRI-based radiomic model achieved a score of 0.822 (95% CI 0.752-0.891).ConclusionsWe demonstrated that pretherapeutic, multiparametric MRI radiomic features have potential in predicting non-response to neoadjuvant therapy in patients with LARC.
DOI10.1245/s10434-019-07300-3
WOS KeywordPATHOLOGICAL COMPLETE RESPONSE ; TUMOR HETEROGENEITY ; PREOPERATIVE CHEMORADIATION ; TEXTURE ANALYSIS ; CHEMORADIOTHERAPY ; MRI ; CHEMOTHERAPY ; RADIOTHERAPY ; RADIATION ; BIOMARKER
Indexed BySCI
Language英语
Funding ProjectBeijing Natural Science Foundation[7182109] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Key Research and Development Plan of China[2017YFA0205200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005]
Funding OrganizationBeijing Natural Science Foundation ; National Natural Science Foundation of China ; National Key Research and Development Plan of China ; Chinese Academy of Sciences
WOS Research AreaOncology ; Surgery
WOS SubjectOncology ; Surgery
WOS IDWOS:000467643800019
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24201
Collection中国科学院自动化研究所
Corresponding AuthorFeng, Yanqiu; Tian, Jie
Affiliation1.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
2.Southern Med Univ, Guangdong Prov Key Lab Med Image Proc, Sch Biomed Engn, Guangzhou, Guangdong, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
4.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou, Guangdong, Peoples R China
5.Sun Yat Sen Univ, Dept Radiol, Sun Yat Sen Mem Hosp, Guangzhou, Guangdong, Peoples R China
6.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou, Henan, Peoples R China
7.Univ Chinese Acad Sci, Beijing, Peoples R China
8.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhou, Xuezhi,Yi, Yongju,Liu, Zhenyu,et al. Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer[J]. ANNALS OF SURGICAL ONCOLOGY,2019,26(6):1676-1684.
APA Zhou, Xuezhi.,Yi, Yongju.,Liu, Zhenyu.,Cao, Wuteng.,Lai, Bingjia.,...&Tian, Jie.(2019).Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer.ANNALS OF SURGICAL ONCOLOGY,26(6),1676-1684.
MLA Zhou, Xuezhi,et al."Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer".ANNALS OF SURGICAL ONCOLOGY 26.6(2019):1676-1684.
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