Paper Info
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.
Year
DOI
Venue
2012
10.1007/s10278-012-9457-7
J. Digital Imaging
Keywords
Field
DocType
Breast,Ultrasonography,Artificial neural network,Breast neoplasm,Logistic regression
Stepwise regression,Receiver operating characteristic,Breast cancer,Breast imaging,Computer science,Cohen's kappa,Radiology,BI-RADS,Logistic regression,Test set
Journal
Volume
Issue
ISSN
25
5
1618-727X
Citations 
PageRank 
References 
0
0.34
0
Authors
12
Name
Order
Citations
PageRank
Sun-Mi Kim1114.02
Heon Han200.34
Jeong Mi Park3554.57
Yoon Jung Choi401.01
Hoi Soo Yoon500.34
Jung Hee Sohn600.34
Moon Hee Baek700.34
Yoon Nam Kim800.34
Young Moon Chae96911.23
Jeon Jong June1000.34
Ji-Won Lee1111612.82
Yong Hwan Jeon1200.34