Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer

doi:10.1245/s10434-019-07300-3

	Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer
	Zhou, Xuezhi 1,3; Yi, Yongju 2; Liu, Zhenyu3,7 ; Cao, Wuteng 4; Lai, Bingjia 5; Sun, Kai 1; Li, Longfei 6; Zhou, Zhiyang 4; Feng, Yanqiu 2; Tian, Jie1,3,7,8
Source Publication	ANNALS OF SURGICAL ONCOLOGY
ISSN	1068-9265
	2019-06-01
Volume	26 Issue:6 Pages:1676-1684
Corresponding Author	Feng, Yanqiu(foree@163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
Abstract	ObjectiveThe 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.
DOI	10.1245/s10434-019-07300-3
WOS Keyword	PATHOLOGICAL COMPLETE RESPONSE ; TUMOR HETEROGENEITY ; PREOPERATIVE CHEMORADIATION ; TEXTURE ANALYSIS ; CHEMORADIOTHERAPY ; MRI ; CHEMOTHERAPY ; RADIOTHERAPY ; RADIATION ; BIOMARKER
Indexed By	SCI
Language	英语
Funding Project	Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[GJJSTD20170004] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81772012] ; Beijing Natural Science Foundation[7182109] ; Beijing 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 Organization	Beijing Natural Science Foundation ; National Natural Science Foundation of China ; National Key Research and Development Plan of China ; Chinese Academy of Sciences
WOS Research Area	Oncology ; Surgery
WOS Subject	Oncology ; Surgery
WOS ID	WOS:000467643800019
Publisher	SPRINGER
Sub direction classification	医学影像处理与分析
Citation statistics	Cited Times:38[WOS] [WOS Record] [Related Records in WOS]
Document Type	期刊论文
Identifier	http://ir.ia.ac.cn/handle/173211/24201
Collection	中国科学院分子影像重点实验室
Corresponding Author	Feng, Yanqiu; Tian, Jie
Affiliation	1.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 Affilication	Institute of Automation, Chinese Academy of Sciences
Corresponding Author Affilication	Institute 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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