Back to Search Start Over

Whole tumour- and subregion-based radiomics of contrast-enhanced mammography in differentiating HER2 expression status of invasive breast cancers: A double-centre pilot study.

Authors :
Wang S
Wang T
Guo S
Zhu S
Chen R
Zheng J
Jiang T
Li R
Li J
Li J
Shen X
Qian M
Yang M
Yu S
You C
Gu Y
Source :
British journal of cancer [Br J Cancer] 2024 Oct 09. Date of Electronic Publication: 2024 Oct 09.
Publication Year :
2024
Publisher :
Ahead of Print

Abstract

Objectives: To explore the value of whole tumour- and subregion-based radiomics of contrast-enhanced mammography (CEM) in differentiating the HER2 expression status of breast cancers.<br />Methods: 352 patients underwent preoperative CEM from two centres were consecutively enroled and divided into the training, internal validation, and external validation cohorts. The lesions were divided into HER2-positive and HER2-negative groups. Besides the radiological features, radiomics features capturing the whole tumour-based (wITH) and subregion-based intratumoral heterogeneity (sITH) were extracted from the craniocaudal view of CEM recombined images. The XGBoost classifier was applied to develop the radiological, sITH, and wITH models. A combined model was constructed by fusing the prediction results of the three models.<br />Results: The mean age of the patients was 51.1 ± 10.7 years. Two radiological features, four wITH features, and three sITH features were selected to establish the models. The combined model significantly improved the AUC to 0.80 ± 0.03 (95% CI: 0.73-0.86), 0.79 ± 0.06 (95% CI: 0.67-0.90), and 0.79 ± 0.05 (95% CI: 0.69-0.89) in the training, internal validation, and external validation cohorts, respectively (All P < 0.05). The combined model showed good agreement between the predicted and observed probabilities and favourable net clinical benefit in the validation cohorts.<br />Conclusions: Both whole tumour- and subregion-based ITH radiomics features of CEM exhibited potential for differentiating the HER2 expression status. Combining conventional radiological features and ITH features can improve the model's performance.<br /> (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Details

Language :
English
ISSN :
1532-1827
Database :
MEDLINE
Journal :
British journal of cancer
Publication Type :
Academic Journal
Accession number :
39379571
Full Text :
https://doi.org/10.1038/s41416-024-02871-9