title: Semiquantitative dynamic contrast-enhanced MRI for accurate classification of complex adnexal masses.
abstract: To identify the best dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) descriptive parameters in predicting malignancy of complex ovarian masses, and develop an optimal decision tree for accurate classification of benign and malignant complex ovarian masses.
  
  
  
  Preoperative DCE-MR images of 55 sonographically indeterminate ovarian masses (27 benign and 28 malignant) were analyzed prospectively. Four descriptive parameters of the dynamic curve, namely, time-to-peak (TTP), wash-in-rate (WIR), relative signal intensity (SIrel), and the initial area under the curve (IAUC60) were calculated on the normalized curves of specified regions-of-interest (ROIs). A two-tailed Student's t-test and two automated classifiers, linear discriminant analysis (LDA) and support vector machines (SVMs), were used to compare the performance of the mentioned parameters individually and in combination with each other.
  
  
  
  TTP (P = 6.15E-8) and WIR (P = 5.65E-5) parameters induced the highest sensitivity (89% for LDA, and 97% for SVM) and specificity (93% for LDA, and 100% for SVM), respectively. Regarding the high sensitivity of TTP and high specificity of WIR and through their combination, an accurate and simple decision-tree classifier was designed using the line equation obtained by LDA classification model. The proposed classifier achieved an accuracy of 89% and area under the ROC curve of 93%.
  
  
  
  In this study an accurate decision-tree classifier based on a combination of TTP and WIR parameters was proposed, which provides a clinically flexible framework to aid radiologists/clinicians to reach a conclusive preoperative diagnosis and patient-specific therapy plan for distinguishing malignant from benign complex ovarian masses.
  
  
  
  2 J. Magn. Reson. Imaging 2017;45:418-427.
authors.affiliation: Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Iran.|Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran.
authors.forename: Anahita Fathi
authors.initials: AF
authors.lastname: Kazerooni
authors.affiliation: Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.|Department of Radiology, Medical Imaging Center, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mahrooz
authors.initials: M
authors.lastname: Malek
authors.affiliation: Department of Diagnostic Imaging, Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Haghighatkhah
authors.affiliation: Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Sara
authors.initials: S
authors.lastname: Parviz
authors.affiliation: Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran.
authors.forename: Mahnaz
authors.initials: M
authors.lastname: Nabil
authors.affiliation: Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Leila
authors.initials: L
authors.lastname: Torbati
authors.affiliation: Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Iran.
authors.forename: Sanam
authors.initials: S
authors.lastname: Assili
authors.affiliation: Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Iran.|Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran.
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Saligheh Rad
authors.affiliation: Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.|Department of Radiology, Medical Imaging Center, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Masoumeh
authors.initials: M
authors.lastname: Gity
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008536:Meglumine; D008875:Middle Aged; D015588:Observer Variation; D009942:Organometallic Compounds; D010049:Ovarian Diseases / Q000000981:diagnostic imaging* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; D008536:Meglumine; C072417:gadoterate meglumine
keywords: classification; complex ovarian masses; decision tree; dynamic contrast-enhanced MR imaging