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.
Area Under Curve ; Biopsy ; Breast Neoplasms* ; Breast* ; Diagnosis* ; Humans ; Information Systems ; Logistic Models ; Retrospective Studies ; Subject Headings ; Ultrasonography*

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a pivotal enzyme in the Toll-like receptor (TLR)/MYD88 dependent signaling pathway, which is highly activated in rheumatoid arthritis tissues and activated B cell-like diffuse large B-cell lymphoma (ABC-DLBCL). Inflammatory responses followed by IRAK4 activation promote B-cell proliferation and aggressiveness of lymphoma. Moreover, proviral integration site for Moloney murine leukemia virus 1 (PIM1) functions as an anti-apoptotic kinase in propagation of ABC-DLBCL with ibrutinib resistance. We developed a dual IRAK4/PIM1 inhibitor KIC-0101 that potently suppresses the NF-κB pathway and proinflammatory cytokine induction in vitro and in vivo. In rheumatoid arthritis mouse models, treatment with KIC-0101 significantly ameliorated cartilage damage and inflammation. KIC-0101 inhibited the nuclear translocation of NF-κB and activation of JAK/STAT pathway in ABC-DLBCLs. In addition, KIC-0101 exhibited an anti-tumor effect on ibrutinib-resistant cells by synergistic dual suppression of TLR/MYD88-mediated NF-κB pathway and PIM1 kinase. Our results suggest that KIC-0101 is a promising drug candidate for autoimmune diseases and ibrutinib-resistant B-cell lymphomas.