Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

Bibliographic Details
Title: Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer
Authors: Si Eun Lee, Yongsik Sim, Sungwon Kim, Eun-Kyung Kim
Source: Ultrasonography, Vol 40, Iss 1, Pp 93-102 (2021)
Publisher Information: Korean Society of Ultrasound in Medicine, 2021.
Publication Year: 2021
Collection: LCC:Medical technology
Subject Terms: breast neoplasms, lymph nodes, ultrasonography, computer-aided design, preoperative period, Medical technology, R855-855.5
More Details: Purpose The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. Methods A total of 496 patients (mean age, 52.5±10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression. Results In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model. Conclusion A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2288-5919
2288-5943
Relation: http://www.e-ultrasonography.org/upload/usg-20026.pdf; https://doaj.org/toc/2288-5919; https://doaj.org/toc/2288-5943
DOI: 10.14366/usg.20026
Access URL: https://doaj.org/article/dcd3a0c72da84417a8ce598168f2bb96
Accession Number: edsdoj.3a0c72da84417a8ce598168f2bb96
Database: Directory of Open Access Journals
More Details
ISSN:22885919
22885943
DOI:10.14366/usg.20026
Published in:Ultrasonography
Language:English