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10.1126/scitranslmed.aaa7582 http://scihub22266oqcxt.onion/10.1126/scitranslmed.aaa7582 C4666025!4666025!26333934     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26333934.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Transl+Med 2015 ; 7 (303): 303ra138 Nephropedia Template TP {{tp|p=26333934|t=2015. Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities |pdf=|usr=}}{{26333934}} gab.com Text Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities JO: Sci Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=26333934 #FOAvip Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative MR imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 patients with de novo, solitary, unilateral GBM. Three distinct phenotypic ?clusters? emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters?pre-multifocal, spherical, and rim-enhancing, names reflecting their image features?were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from analysis of TCGA tumor copy number and gene expression data with the PARADIGM algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM. Twit Text FOAVip Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities ????Sci Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=26333934 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26333934 #FOAvip English Wikipedia <ref>{{Cite pmid|26333934}}</ref> Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities #MMPMID26333934 Itakura H; Achrol AS; Mitchell LA; Loya JJ; Liu T; Westbroek EM; Feroze AH; Rodriguez S; Echegaray S; Azad TD; Yeom KW; Napel S; Rubin DL; Chang SD; Harsh GR; Gevaert O Sci Transl Med 2015[Sep]; 7 (303): 303ra138 PMID26333934show ga Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative MR imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 patients with de novo, solitary, unilateral GBM. Three distinct phenotypic ?clusters? emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters?pre-multifocal, spherical, and rim-enhancing, names reflecting their image features?were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from analysis of TCGA tumor copy number and gene expression data with the PARADIGM algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM. äMagnetic resonance image features identify glioblastoma phenotypic sub(epmc).pdf DeepDyve Pubget Overpricing