title: A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.
abstract: To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.
authors.affiliation: Department of Radiology, Kangwon National University College of Medicine, 192-1 Hyoja 2-dong, Chuncheon, Kangwon-do, 200-701, Republic of Korea.
authors.forename: Sun Mi
authors.initials: SM
authors.lastname: Kim
authors.forename: Heon
authors.initials: H
authors.lastname: Han
authors.forename: Jeong Mi
authors.initials: JM
authors.lastname: Park
authors.forename: Yoon Jung
authors.initials: YJ
authors.lastname: Choi
authors.forename: Hoi Soo
authors.initials: HS
authors.lastname: Yoon
authors.forename: Jung Hee
authors.initials: JH
authors.lastname: Sohn
authors.forename: Moon Hee
authors.initials: MH
authors.lastname: Baek
authors.forename: Yoon Nam
authors.initials: YN
authors.lastname: Kim
authors.forename: Young Moon
authors.initials: YM
authors.lastname: Chae
authors.forename: Jeon Jong
authors.initials: JJ
authors.lastname: June
authors.forename: Jiwon
authors.initials: J
authors.lastname: Lee
authors.forename: Yong Hwan
authors.initials: YH
authors.lastname: Jeon
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003628:Database Management Systems; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016015:Logistic Models; D008875:Middle Aged; D016571:Neural Networks, Computer; D015588:Observer Variation; D012372:ROC Curve; D056910:Republic of Korea; D012189:Retrospective Studies; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult