Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography.
- Author:
Sun Mi KIM
1
;
Yongdai KIM
;
Kuhwan JEONG
;
Heeyeong JEONG
;
Jiyoung KIM
Author Information
- Publication Type:Original Article
- Keywords: Ultrasonography; Breast; Logistic models; Diagnosis; Breast neoplasms
- MeSH: Area Under Curve; Biopsy; Breast Neoplasms*; Breast*; Diagnosis*; Humans; Information Systems; Logistic Models; Retrospective Studies; Subject Headings; Ultrasonography*
- From: Ultrasonography 2018;37(1):36-42
- CountryRepublic of Korea
- Language:English
- Abstract: PURPOSE: The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. METHODS: This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. RESULTS: Logistic LASSO regression was superior (P < 0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P < 0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P < 0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). CONCLUSION: Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.
