DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers

Handle URI:
http://hdl.handle.net/10754/626348
Title:
DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers
Authors:
Fan, Ming; Cheng, Hu; Zhang, Peng; Gao, Xin ( 0000-0002-7108-3574 ) ; Zhang, Juan; Shao, Guoliang; Li, Lihua
Abstract:
Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Retrospective study.Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression.T1 -weighted 3.0T DCE-MR images.Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed.Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor.In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59).Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis.4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Fan M, Cheng H, Zhang P, Gao X, Zhang J, et al. (2017) DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers. Journal of Magnetic Resonance Imaging. Available: http://dx.doi.org/10.1002/jmri.25921.
Publisher:
Wiley-Blackwell
Journal:
Journal of Magnetic Resonance Imaging
Issue Date:
8-Dec-2017
DOI:
10.1002/jmri.25921
Type:
Article
ISSN:
1053-1807
Sponsors:
Contract grant sponsor: National Natural Science Foundation of China; contract grant numbers: 61401131; 61731008; 61271063; Contract grant sponsor: Natural Science Foundation of Zhejiang Province of China; contract grant number: LZ15F010001; Contract grant sponsor: King Abdullah University of Science and Technology (KAUST).
Additional Links:
http://onlinelibrary.wiley.com/doi/10.1002/jmri.25921/full
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorFan, Mingen
dc.contributor.authorCheng, Huen
dc.contributor.authorZhang, Pengen
dc.contributor.authorGao, Xinen
dc.contributor.authorZhang, Juanen
dc.contributor.authorShao, Guoliangen
dc.contributor.authorLi, Lihuaen
dc.date.accessioned2017-12-11T08:42:20Z-
dc.date.available2017-12-11T08:42:20Z-
dc.date.issued2017-12-08en
dc.identifier.citationFan M, Cheng H, Zhang P, Gao X, Zhang J, et al. (2017) DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers. Journal of Magnetic Resonance Imaging. Available: http://dx.doi.org/10.1002/jmri.25921.en
dc.identifier.issn1053-1807en
dc.identifier.doi10.1002/jmri.25921en
dc.identifier.urihttp://hdl.handle.net/10754/626348-
dc.description.abstractBreast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Retrospective study.Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression.T1 -weighted 3.0T DCE-MR images.Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed.Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor.In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59).Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis.4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.en
dc.description.sponsorshipContract grant sponsor: National Natural Science Foundation of China; contract grant numbers: 61401131; 61731008; 61271063; Contract grant sponsor: Natural Science Foundation of Zhejiang Province of China; contract grant number: LZ15F010001; Contract grant sponsor: King Abdullah University of Science and Technology (KAUST).en
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1002/jmri.25921/fullen
dc.rightsThis is the peer reviewed version of the following article: DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers, which has been published in final form at http://doi.org/10.1002/jmri.25921. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.en
dc.subjectBreast canceren
dc.subjectKi-67en
dc.subjectDce-mrien
dc.subjectTumor Partitioningen
dc.titleDCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancersen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Magnetic Resonance Imagingen
dc.eprint.versionPost-printen
dc.contributor.institutionInstitute of Biomedical Engineering and Instrumentation; Hangzhou Dianzi University; Hangzhou Chinaen
dc.contributor.institutionZhejiang Cancer Hospital; Zhejiang Hangzhou Chinaen
kaust.authorGao, Xinen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.