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10.1038/srep11085 http://scihub22266oqcxt.onion/10.1038/srep11085 C4457139!4457139 !26046791     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 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\26046791 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2015 ; 5 (ä): 11085 Nephropedia Template TP {{tp|p=26046791 |t=2015. Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision |pdf=|usr=}}{{26046791 }} gab.com Text Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26046791 #FOAvip Breast cancer is molecularly heterogeneous and categorized into four molecular subtypes: Luminal-A, Luminal-B, HER2-amplified and Triple-negative. In this study, we aimed to apply an ensemble decision approach to identify the ultrasound and clinical features related to the molecular subtypes. We collected ultrasound and clinical features from 1,000 breast cancer patients and performed immunohistochemistry on these samples. We used the ensemble decision approach to select unique features and to construct decision models. The decision model for Luminal-A subtype was constructed based on the presence of an echogenic halo and post-acoustic shadowing or indifference. The decision model for Luminal-B subtype was constructed based on the absence of an echogenic halo and vascularity. The decision model for HER2-amplified subtype was constructed based on the presence of post-acoustic enhancement, calcification, vascularity and advanced age. The model for Triple-negative subtype followed two rules. One was based on irregular shape, lobulate margin contour, the absence of calcification and hypovascularity, whereas the other was based on oval shape, hypovascularity and micro-lobulate margin contour. The accuracies of the models were 83.8%, 77.4%, 87.9% and 92.7%, respectively. We identified specific features of each molecular subtype and expanded the scope of ultrasound for making diagnoses using these decision models. Twit Text FOAVip Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26046791 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26046791 #FOAvip English Wikipedia <ref>{{Cite pmid|26046791 }}</ref> Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision #MMPMID26046791 Zhang L ; Li J ; Xiao Y ; Cui H ; Du G ; Wang Y ; Li Z ; Wu T ; Li X ; Tian J Sci Rep 2015[Jun]; 5 (ä): 11085 PMID26046791 show ga Breast cancer is molecularly heterogeneous and categorized into four molecular subtypes: Luminal-A, Luminal-B, HER2-amplified and Triple-negative. In this study, we aimed to apply an ensemble decision approach to identify the ultrasound and clinical features related to the molecular subtypes. We collected ultrasound and clinical features from 1,000 breast cancer patients and performed immunohistochemistry on these samples. We used the ensemble decision approach to select unique features and to construct decision models. The decision model for Luminal-A subtype was constructed based on the presence of an echogenic halo and post-acoustic shadowing or indifference. The decision model for Luminal-B subtype was constructed based on the absence of an echogenic halo and vascularity. The decision model for HER2-amplified subtype was constructed based on the presence of post-acoustic enhancement, calcification, vascularity and advanced age. The model for Triple-negative subtype followed two rules. One was based on irregular shape, lobulate margin contour, the absence of calcification and hypovascularity, whereas the other was based on oval shape, hypovascularity and micro-lobulate margin contour. The accuracies of the models were 83.8%, 77.4%, 87.9% and 92.7%, respectively. We identified specific features of each molecular subtype and expanded the scope of ultrasound for making diagnoses using these decision models. |Adolescent [MESH] |Adult [MESH] |Age Factors [MESH] |Aged [MESH] |Algorithms [MESH] |Breast Neoplasms/*classification/diagnostic imaging/pathology [MESH] |Child [MESH] |Female [MESH] |Humans [MESH] |Immunohistochemistry [MESH] |Ki-67 Antigen/metabolism [MESH] |Middle Aged [MESH] |Receptor, ErbB-2/metabolism [MESH] |Receptors, Estrogen/metabolism [MESH] |Receptors, Progesterone/metabolism [MESH] |Ultrasonography [MESH]Identifying ultrasound and clinical features of breast cancer molecula(epmc).pdf DeepDyve Pubget Overpricing