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Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics

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Abstract

Objectives

To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer.

Methods

A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n1 = 407) and an independent validation dataset (n1 = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively.

Conclusions

Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients.

Clinical relevance statement

The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies.

Key Points

• Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy.

• Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers.

• Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

DCE:

Dynamic contrast-enhanced

DWI:

Diffusion-weighted imaging

ER:

Estrogen receptor

FS:

Fat suppression

HER-2:

Human epidermal growth factor receptor-2

ICC:

Intra-class correlation coefficient

IHC:

Immunohistochemistry

ISH:

In situ hybridization

LASSO:

Least absolute shrinkage and selection operator

PR:

Progesterone receptor

ROC:

Receiver operating characteristic

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

VOI:

Volume of interest

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Acknowledgements

The authors thank the colleagues from Shantou Central Hospital and Sun Yat-Sen Memorial Hospital for their constructive suggestions in the conception and completion of this work.

Funding

This study has received funding from the National Natural Science Foundation of China (82102130, 12126610), R&D project of Pazhou Lab (Huangpu) under Grant 2023K0606, Guangdong Basic and Applied Basic Research Foundation (2023A1515011305), Guangdong Medical Research Foundation (B2023426), Guangzhou Basic and Applied Basic Research Foundation (2023A04J2112), and Xinjiang Uygur Autonomous Region Tianshan Talent Youth Science and Technology Top Talent Project (2022TSYCJC0011).

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Authors

Corresponding authors

Correspondence to Daiying Lin or Xiang Zhang.

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Guarantor

The scientific guarantor of this publication is Xiang Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One author (Zehong Yang) has statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained from the Institutional Review Board of Shantou Central Hospital (Shantou, China) ([2022] Research 072), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University (Guangzhou, China) (SYSKY-2023–788-01).

Study subjects or cohorts overlap

No study subject or cohort has been previously reported in this study.

Methodology

• retrospective

• diagnostic study

• multicenter study

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Zheng, S., Yang, Z., Du, G. et al. Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10641-7

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  • DOI: https://doi.org/10.1007/s00330-024-10641-7

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