Back to Search Start Over

Gail model and fifth edition of ultrasound BI‐RADS help predict axillary lymph node metastasis in breast cancer—A multicenter prospective study.

Authors :
Gao, Lu‐Ying
Ran, Hai‐Tao
Deng, You‐Bin
Luo, Bao‐Ming
Zhou, Ping
Chen, Wu
Zhang, Yu‐Hong
Li, Jian‐Chu
Wang, Hong‐Yan
Jiang, Yu‐Xin
Source :
Asia Pacific Journal of Clinical Oncology; Apr2023, Vol. 19 Issue 2, pe71-e79, 9p
Publication Year :
2023

Abstract

Rationale and Objectives: We aim to assess the performance of the Gail model and the fifth edition of ultrasound BI‐RADS (Breast Imaging Reporting and Data System) in breast cancer for predicting axillary lymph node metastasis (ALNM). Materials and Methods: We prospectively studied 958 female patients with breast cancer between 2018 and 2019 from 35 hospitals in China. Based on B‐mode, color Doppler, and elastography, radiologists classified the degree of suspicion based on the fifth edition of BI‐RADS. Individual breast cancer risk was assessed with the Gail model. The association between the US BI‐RADS category and the Gail model in terms of ALNM was analyzed. Results: We found that US BI‐RADS category was significantly and independently associated with ALNM (P < 0.001). The sensitivity, specificity, and accuracy of BI‐RADS category 5 for predicting ALNM were 63.6%, 71.6%, and 68.6%, respectively. Combining the Gail model with the BI‐RADS category showed a significantly higher sensitivity than using the BI‐RADS category alone (67.8% vs. 63.6%, P < 0.001). The diagnostic accuracy of the BI‐RADS category combined with the Gail model was better than that of the Gail model alone (area under the curve: 0.71 vs. 0.50, P < 0.001). Conclusion: Based on the conventional ultrasound and elastography, the fifth edition of ultrasound BI‐RADS category could be used to predict the ALNM of breast cancer. ALNM was likely to occur in patients with BI‐RADS category 5. The Gail model could improve the diagnostic sensitivity of the US BI‐RADS category for predicting ALNM in breast cancer. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17437555
Volume :
19
Issue :
2
Database :
Complementary Index
Journal :
Asia Pacific Journal of Clinical Oncology
Publication Type :
Academic Journal
Accession number :
162823838
Full Text :
https://doi.org/10.1111/ajco.13781