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MRI-based vector radiomics for predicting breast cancer HER2 status and its changes after neoadjuvant therapy.

Authors :
Zhang L
Cui QX
Zhou LQ
Wang XY
Zhang HX
Zhu YM
Sang XQ
Kuai ZX
Source :
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2024 Oct 17; Vol. 118, pp. 102443. Date of Electronic Publication: 2024 Oct 17.
Publication Year :
2024
Publisher :
Ahead of Print

Abstract

Purpose: To develop a novel MRI-based vector radiomic approach to predict breast cancer (BC) human epidermal growth factor receptor 2 (HER2) status (zero, low, and positive; task 1) and its changes after neoadjuvant therapy (NAT) (positive-to-positive, positive-to-negative, and positive-to-pathologic complete response; task 2).<br />Materials and Methods: Both dynamic contrast-enhanced (DCE) MRI data and multi-b-value (MBV) diffusion-weighted imaging (DWI) data were acquired in BC patients at two centers. Vector-radiomic and conventional-radiomic features were extracted from both DCE-MRI and MBV-DWI. After feature selection, the following models were built using the retained features and logistic regression: vector model, conventional model, and combined model that integrates the vector-radiomic and conventional-radiomic features. The models' performances were quantified by the area under the receiver-operating characteristic curve (AUC).<br />Results: The training/external test set (center 1/2) included 483/361 women. For task 1, the vector model (AUCs=0.73∼0.86) was superior to (p<.05) the conventional model (AUCs=0.68∼0.81), and the addition of vector-radiomic features to conventional-radiomic features yielded an incremental predictive value (AUCs=0.80∼0.90, p<.05). For task 2, the combined MBV-DWI model (AUCs=0.85∼0.89) performed better than (p<.05) the conventional MBV-DWI model (AUCs=0.73∼0.82). In addition, for the combined DCE-MRI model and the combined MBV-DWI model, the former (AUCs=0.85∼0.90) outperformed (p<.05) the latter (AUCs=0.80∼0.85) in task 1, whereas the latter (AUCs=0.85∼0.89) outperformed (p<.05) the former (AUCs=0.76∼0.81) in task 2. The above results are true for the training and external test sets.<br />Conclusions: MRI-based vector radiomics may predict BC HER2 status and its changes after NAT and provide significant incremental prediction over and above conventional radiomics.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1879-0771
Volume :
118
Database :
MEDLINE
Journal :
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Publication Type :
Academic Journal
Accession number :
39427545
Full Text :
https://doi.org/10.1016/j.compmedimag.2024.102443