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Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer

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Abstract

Objective

This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.

Methods

A total of 481 patients with breast cancer who underwent preoperative MRI were retrospectively recruited from two institutions. Traditional radiomics features and deep semantic segmentation feature-based radiomics (DSFR) features were extracted from segmented tumors to construct models separately. Then, the DLR model was constructed to assess the HER2 status by averaging the output probabilities of the two models. Finally, a Kaplan‒Meier survival analysis was conducted to explore the disease-free survival (DFS) in patients with HER2-low-positive status. The multivariate Cox proportional hazard model was constructed to further determine the factors associated with DFS.

Results

First, the DLR model distinguished between HER2-negative and HER2-overexpressing patients with AUCs of 0.868 and 0.763 in the training and validation cohorts, respectively. Furthermore, the DLR model distinguished between HER2-low-positive and HER2-zero patients with AUCs of 0.855 and 0.750, respectively. Cox regression analysis showed that the prediction score obtained using the DLR model (HR, 0.175; p = 0.024) and lesion size (HR, 1.043; p = 0.009) were significant, independent predictors of DFS.

Conclusions

We successfully constructed a DLR model based on MRI to noninvasively evaluate the HER2 status and further revealed prospects for predicting the DFS of patients with HER2-low-positive status.

Clinical relevance statement

The MRI-based DLR model could noninvasively identify HER2-low-positive status, which is considered a novel prognostic predictor and therapeutic target.

Key Points

• The DLR model effectively distinguished the HER2 status of breast cancer patients, especially the HER2-low-positive status.

• The DLR model was better than the traditional radiomics model or DSFR model in distinguishing HER2 expression.

• The prediction score obtained using the model and lesion size were significant independent predictors of DFS.

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Abbreviations

ACC:

Accuracy

ADCs:

Antibody–drug conjugates

AIC:

Akaike information criterion

ASCO:

American Society of Clinical Oncology

CAP:

College of American Pathologists

CE-MRI:

Contrast-enhanced MRI

DFS:

Disease-free survival

DLR:

Deep learning radiomics

DSFR:

Deep semantic segmentation feature-based radiomics

FISH:

Fluorescent in situ hybridization

HER2:

Human epidermal growth factor receptor 2

HR:

Hazard ratio

ICC:

Intraclass correlation coefficients

IHC:

Immunohistochemistry

ISH:

In situ hybridization

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

ROC:

Receiver operating characteristic

ROI:

Region of interest

SEN:

Sensitivity

SPE:

Specificity

TE/TR:

Echo time / repetition time

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Funding

This study was supported by (1) the National Natural Science Foundation of China, No. 81901711, 82171920, 62271448; (2) the Special Fund for the Construction of High-level Key Clinical Specialty (Medical Imaging) in Guangzhou, Guangzhou Key Laboratory of Molecular Imaging and Clinical Translational Medicine; (3) Guangdong Basic and Applied Basic Research Foundation, No. 2022A1515110792; (4) Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, No. 2023SHIBS0003; (5) Guangzhou First People's Hospital Frontier Medical Technology Project (QY-C04). (6) Science and Technology Projects in Guangzhou, No. SL2022A04J00652, 202201020001, 202201010513.

Author information

Author notes

  1. Yuan Guo, Xiaotong Xie, and Wenjie Tang contributed equally to this work

Authors and Affiliations

  1. Department of Radiology, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China

    Yuan Guo, Wenjie Tang, Siyi Chen, Wenke Hu, Xinhua Wei & Xinqing Jiang

  2. School of Life Science, South China Normal University, Guangzhou, 510631, China

    Xiaotong Xie

  3. Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, Guangdong, 518055, China

    Xiaotong Xie, Mingyu Wang, Yaheng Fan, Chuxuan Lin & Bingsheng Huang

  4. Department of Pathology, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China

    Jing Yang

  5. Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, Guangdong, 510010, China

    Jialin Xiang & Kuiming Jiang

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  1. Yuan Guo
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Corresponding authors

Correspondence to Kuiming Jiang, Xinhua Wei, Bingsheng Huang or Xinqing Jiang.

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Guarantor

The scientific guarantor of this publication is Xin-qing Jiang.

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

Xiaotong Xie, Mingyu Wang, and Bingsheng Huang kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Guo, Y., Xie, X., Tang, W. et al. Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer. Eur Radiol 34, 899–913 (2024). https://doi.org/10.1007/s00330-023-09990-6

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