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Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography

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Abstract

Objectives

We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications.

Methods

We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists’ diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI.

Results

The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists’ ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists’ MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B.

Conclusion

The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI.

Clinical relevance statement

The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved.

Key Points

• We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported.

• Our multi-modality model outperformed both the radiologists’ ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography.

• The multi-modality model can serve as a potential decision support tool to improve the radiologists’ ALN diagnosis on MRI.

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Abbreviations

ACC:

Accuracy

AI:

Artificial intelligence

ALN:

Axillary lymph node

ALND:

Axillary lymph node dissection

ALNM:

Axillary lymph node metastasis

AUC:

Area under the curve

AUS:

Axillary ultrasound

DSC:

Dice similarity coefficient score

HER2:

Human epidermal growth factor receptor 2

HR:

Hormonal receptor

ICC:

The intraclass correlation coefficient

IHC:

Immunohistochemical

LASSO:

Least absolute shrinkage and selection operator

MMG:

Mammography

MRI:

Magnetic resonance imaging

NPV:

Negative predictive value

PPV:

Positive predictive value

SEN:

Sensitivity

SLNB:

Sentinel lymph node biopsy

SPE:

Specificity

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Acknowledgements

We thank all the study participants for their participation in this study, especially thanks Genji Bai, Yingyu Lin, and Cong Ding for providing technical support to the study.

Funding

The authors state that this work has not received any funding.

Author information

Author notes

  1. Qian Wang and Yingyu Lin contributed equally to this work and share co-first authorship.

Authors and Affiliations

  1. Department of Radiology, The Affiliated Huaian Clinical College of Xuzhou Medical University, Huaian, Jiangsu, China

    Qian Wang & Genji Bai

  2. Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China

    Yingyu Lin & Xiaoling Zhang

  3. Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China

    Cong Ding, Wenting Guan, Jianye Jia, Wei Zhou, Ziyan Liu & Genji Bai

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  1. Qian Wang
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Corresponding author

Correspondence to Genji Bai.

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The scientific guarantor of this publication is Genji, Bai.

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

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• diagnostic or prognostic study

• multicenter study

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Wang, Q., Lin, Y., Ding, C. et al. Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10638-2

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