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Integrated nomogram to predict HER2 expression in breast tumor: Clinical, Ultrasound, and Photoacoustic imaging approaches.

Authors :
Chen J
Yin Y
Li G
Tian H
Ding Z
Mo S
Xu J
Huang Z
Dong F
Source :
European journal of cancer (Oxford, England : 1990) [Eur J Cancer] 2024 Sep; Vol. 209, pp. 114259. Date of Electronic Publication: 2024 Aug 03.
Publication Year :
2024

Abstract

Background: HER2 is a key biomarker for breast cancer treatment and prognosis. Traditional assessment methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are effective but costly and time-consuming. Our model incorporates these methods alongside photoacoustic imaging to enhance diagnostic accuracy and provide more comprehensive clinical insights.<br />Materials and Methods: A total of 301 breast tumors were included in this study, divided into HER2-positive (3+ or 2+ with gene amplification) and HER2-negative (below 3+ and 2+ without gene amplification) groups. Samples were split into training and validation sets in a 7:3 ratio. Statistical analyses involved t-tests, chi-square tests, and rank-sum tests. Predictive factors were identified using univariate and multivariate logistic regression, leading to the creation of three models: ModA (clinical factors only), ModB (clinical plus ultrasound factors), and ModC (clinical, ultrasound, and photoacoustic imaging-derived oxygen saturation (SO2)).<br />Results: The area under the curve (AUC) for ModA was 0.756 (95 % CI: 0.69-0.82), ModB increased to 0.866 (95 % CI: 0.82-0.91), and ModC showed the highest performance with an AUC of 0.877 (95 % CI: 0.83-0.92). These results indicate that the comprehensive model combining clinical, ultrasound, and photoacoustic imaging data (ModC) performed best in predicting HER2 expression.<br />Conclusion: The findings suggest that integrating clinical, ultrasound, and photoacoustic imaging data significantly enhances the accuracy of predicting HER2 expression. For personalised breast cancer treatment, the integrated model could provide a comprehensive and reproducible decision support tool.<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 :
1879-0852
Volume :
209
Database :
MEDLINE
Journal :
European journal of cancer (Oxford, England : 1990)
Publication Type :
Academic Journal
Accession number :
39111206
Full Text :
https://doi.org/10.1016/j.ejca.2024.114259