Cain, Elizabeth Hope <br/><br/>
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.  [electronic resource] 

 -  Breast cancer research and treatment  Jan 2019 
 - 455-463 p.  digital 
<br/><br/> Publication Type: Journal Article; Validation Study 
<br/><br/><label>ISSN: </label>1573-7217 
<br/><br/><label>Standard No.: </label>10.1007/s10549-018-4990-9  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Adult<br/>Aged<br/>Antineoplastic Combined Chemotherapy Protocols--therapeutic use<br/>Breast--diagnostic imaging<br/>Feasibility Studies<br/>Female<br/>Humans<br/>Image Processing, Computer-Assisted--methods<br/>Machine Learning<br/>Magnetic Resonance Imaging<br/>Mastectomy, Segmental<br/>Middle Aged<br/>Neoadjuvant Therapy--methods<br/>Neoplasm Staging<br/>ROC Curve<br/>Receptor, ErbB-2--metabolism<br/>Retrospective Studies<br/>Treatment Outcome<br/>Triple Negative Breast Neoplasms--diagnostic imaging