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10.1148/radiol.14131375 http://scihub22266oqcxt.onion/10.1148/radiol.14131375 C4564060!4564060!24702725     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Radiology 2014 ; 272 (2): 374-84 Nephropedia Template TP {{tp|p=24702725|t=2014. Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles1 |pdf=|usr=}}{{24702725}} gab.com Text Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles1 JO: Radiology http://www.kidney.de/pget.php?&tw=1&pmid=24702725 #FOAvip Purpose: To present a method for identifying intrinsic imaging phenotypes in breast cancer tumors and to investigate their association with prognostic gene expression profiles. Materials and Methods: The authors retrospectively analyzed dynamic contrast material?enhanced (DCE) magnetic resonance (MR) images of the breast in 56 women (mean age, 55.6 years; age range, 37?74 years) diagnosed with estrogen receptor?positive breast cancer between 2005 and 2010. The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Primary tumors were assayed with a validated gene expression assay that provides a score for the likelihood of recurrence. A multiparametric imaging phenotype vector was extracted for each tumor by using quantitative morphologic, kinetic, and spatial heterogeneity features. Multivariate linear regression was performed to test associations between DCE MR imaging features and recurrence likelihood. To identify intrinsic imaging phenotypes, hierarchical clustering was performed on the extracted feature vectors. Multivariate logistic regression was used to classify tumors at high versus low or medium risk of recurrence. To determine the additional value of intrinsic phenotypes, the phenotype category was tested as an additional variable. Receiver operating characteristic analysis and the area under the receiver operating characteristic curve (Az) were used to assess classification performance. Results: There was a moderate correlation (r = 0.71, R2 = 0.50, P < .001) between DCE MR imaging features and the recurrence score. DCE MR imaging features were predictive of recurrence risk as determined by the surrogate assay, with an Az of 0.77 (P < .01). Four dominant imaging phenotypes were detected, with two including only low- and medium-risk tumors. When the phenotype category was used as an additional variable, the Az increased to 0.82 (P < .01). Conclusion: Intrinsic imaging phenotypes exist for breast cancer tumors and correlate with recurrence likelihood as determined with gene expression profiling. These imaging biomarkers could ultimately help guide treatment decisions. Twit Text FOAVip Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles1 ????Radiology http://www.kidney.de/pget.php?&tw=1&pmid=24702725 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24702725 #FOAvip English Wikipedia <ref>{{Cite pmid|24702725}}</ref> Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles1 #MMPMID24702725 Ashraf AB; Daye D; Gavenonis S; Mies C; Feldman M; Rosen M; Kontos D Radiology 2014[Aug]; 272 (2): 374-84 PMID24702725show ga Purpose: To present a method for identifying intrinsic imaging phenotypes in breast cancer tumors and to investigate their association with prognostic gene expression profiles. Materials and Methods: The authors retrospectively analyzed dynamic contrast material?enhanced (DCE) magnetic resonance (MR) images of the breast in 56 women (mean age, 55.6 years; age range, 37?74 years) diagnosed with estrogen receptor?positive breast cancer between 2005 and 2010. The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Primary tumors were assayed with a validated gene expression assay that provides a score for the likelihood of recurrence. A multiparametric imaging phenotype vector was extracted for each tumor by using quantitative morphologic, kinetic, and spatial heterogeneity features. Multivariate linear regression was performed to test associations between DCE MR imaging features and recurrence likelihood. To identify intrinsic imaging phenotypes, hierarchical clustering was performed on the extracted feature vectors. Multivariate logistic regression was used to classify tumors at high versus low or medium risk of recurrence. To determine the additional value of intrinsic phenotypes, the phenotype category was tested as an additional variable. Receiver operating characteristic analysis and the area under the receiver operating characteristic curve (Az) were used to assess classification performance. Results: There was a moderate correlation (r = 0.71, R2 = 0.50, P < .001) between DCE MR imaging features and the recurrence score. DCE MR imaging features were predictive of recurrence risk as determined by the surrogate assay, with an Az of 0.77 (P < .01). Four dominant imaging phenotypes were detected, with two including only low- and medium-risk tumors. When the phenotype category was used as an additional variable, the Az increased to 0.82 (P < .01). Conclusion: Intrinsic imaging phenotypes exist for breast cancer tumors and correlate with recurrence likelihood as determined with gene expression profiling. These imaging biomarkers could ultimately help guide treatment decisions. äIdentification of Intrinsic Imaging Phenotypes for Breast Cancer Tumor(epmc).pdf DeepDyve Pubget Overpricing