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Clinical Studies

A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial

British Journal of Cancer volume 131, pages 692–701 (2024)Cite this article

Subjects

Abstract

Background

This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer.

Methods

We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann–Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC.

Results

This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05).

Conclusions

The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists.

Trial registration number

The clinical trial numbers were NCT03154749 and NCT04858529.

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Fig. 1: The study design and workflow.
Fig. 2: Model performance metrics across training and validation cohorts.
Fig. 3: Comparison of radiologist and AI in predicting axillary lymph node response and disease-free survival.
Fig. 4: ROC curves of models for subtype-specific predictions across cohorts.

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author Kun Wang, upon reasonable request.

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Funding

This study is supported by grants from the National Natural Science Foundation of China (82171898), Deng Feng Project of High-level Hospital Construction (DFJHBF202109), Guangdong Basic and Applied Basic Research Foundation (grant number 2022A1515012277, 2023A1515010222), Guangzhou Science and Technology Project (202002030236), Macao Science and Technology Development Fund (20210701181316106/AKP), Beijing Medical Award Foundation (YXJL-2020-0941-0758), Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5), Development Cancer for Medical Science and Technology National Health Commission of the People’s Republic of China (WKZX2023CX110002), and Beijing Life Oasis Public Service Center (cphcf-2022-058). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

Author information

Author notes

  1. These authors contributed equally: Teng Zhu, Yu-Hong Huang, Wei Li, Can-Gui Wu.

Authors and Affiliations

  1. Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China

    Teng Zhu, Yu-Hong Huang, Can-Gui Wu, Xing-Xing Zheng, Ting-Feng Zhang, Ying-Yi Lin & Kun Wang

  2. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China

    Wei Li

  3. Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China

    Yi-Min Zhang & Zhi-Yong Wu

  4. Shantou University Medical College, Shantou, 515041, Guangdong, China

    Ying-Yi Lin

  5. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China

    Zai-Yi Liu

  6. Department of Breast Cancer, The First People’s Hospital of Foshan, Foshan, Guangdong, China

    Guo-Lin Ye

  7. Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Ying Lin

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Contributions

Teng Zhu contributed to the conception design, collection and analysis of data, and manuscript writing. Yu-Hong Huang contributed to the data analysis, data interpretation and manuscript writing. Wei Li contributed to the collection and analysis of data, and manuscript writing. Can-Gui Wu contributed to the analysis of data, and manuscript writing. Yi-Min Zhang contributed to the conception design. Xing-Xing Zheng contributed to the analysis of pathology. Ting-Feng Zhang contributed to the provision of study materials of patients. Ying-Yi Lin and Zhi-Yong Wu contributed to the provision of study materials of patients and data proofreading. Zai-Yi Liu, Guo-Lin Ye and Ying Lin contributed to the administrative support, provision of study materials of patients and manuscript revision. Kun Wang contributed to the conception design, funding acquisition and manuscript revision. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhi-Yong Wu or Kun Wang.

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The authors declare no competing interests.

Ethics approval and consent to participate

This multicenter study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Guangdong Provincial People’s Hospital and Institute (2019764H), and all patients in this study consented to the use of their past radiological and pathological data for research purposes.

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Zhu, T., Huang, YH., Li, W. et al. A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial. Br J Cancer 131, 692–701 (2024). https://doi.org/10.1038/s41416-024-02726-3

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