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MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study

  • Breast
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Abstract

Objectives

Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status.

Methods

We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status.

Results

The AUCs of the radiomic-based model were 0.78 (0.66–0.90), 0.88 (0.79–0.96), and 0.76 (0.68–0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0–10% and 27.3–87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area.

Conclusions

This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status.

Clinical relevance statement

This study can assist the pre-operative planning of BCS. Further research on the tumor immune microenvironment of different resection margin states is expected to develop new margin evaluation indicators and decipher the internal mechanism.

Key Points

• The MRI-based radiomic prediction model (CSS model) incorporating features extracted from multiple sequences and segments could estimate the margin positivity risk of breast-conserving surgery.

• The radiomic score of the CSS model allows risk stratification of patients undergoing breast-conserving surgery, which could assist in surgical planning.

• With the help of MRI-based radiomics to estimate the components of the immune microenvironment, for the first time, it is found that the margin status of breast-conserving surgery is associated with the infiltration of immune cells in the microenvironment and the EMT status of breast tumor cells.

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Abbreviations

BCS:

Breast-conserving surgery

CRC:

Colorectal cancer

CSS model:

Comb_Seg_Seq model

DCIS:

Ductal carcinoma in situ

DEGs:

Differentially expressed genes

EMT:

Epithelial-mesenchymal transition

ER:

Estrogen receptor

EIC:

Extensive intraductal component

FSA:

Frozen-section analysis

HER-2:

Human epidermal growth factor receptor 2

HR:

Hormone receptor

iDCs:

Immature dendritic cells

NPV:

Negative predictive value

PR:

Progesterone receptor

RC model:

Radiomics-Clinicopathological Model

RFE:

Recursive feature elimination

TB:

Tumor budding

TCGA:

The Cancer Genome Atlas

TiME:

Tumor immune microenvironment

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Acknowledgements

We appreciate the assistance from the Disease Registry Department, the Artificial Intelligence Lab and the Big Data Center of Sun Yat-sen Memorial Hospital, Sun Yat-sen University. We also appreciate the support from REDCap development team and research teams of Vanderbilt University Medical Center.

Funding

This work was supported by grants from the Natural Science Foundation of China (81621004, 92159303, 81720108029, 81930081, 91940305), Guangdong Science and Technology Department (2020B1212060018, 2020B1212030004, 2019A1515110075), Department of Natural Resources of Guangdong Province (GDNRC[2021]51), Clinical Innovation Research Program of Bioland Laboratory (2018GZR0201004), Bureau of Science and Technology of Guangzhou (20212200003, 202102010221), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (2019BT02Y198), the Yat-sen Scholarship of Young Scientist program of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, and by the grants from the Sun Yat-sen Clinical Research Cultivating Program of Sun Yat-sen Memorial Hospital, Sun Yat-sen University(#SYS-Q-202002), the Sun Yat-Sen University Clinical Research 5010 Program (#2018022), as well as by National Natural Science Foundation of Guangdong Province (#2019A1515011467, #2021A1515012361, #2019A1515110075), grants from the National Institute of Hospital Administration (#RXDBZ-2022-04) and the grants from the China Anti-aging Promoting Association.

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Author notes

  1. Jiafan Ma, Kai Chen, and Shunrong Li share equal contribution.

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China

    Jiafan Ma, Kai Chen, Shunrong Li, Liling Zhu, Zhilong Pan, Qiang Liu & Erwei Song

  2. Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China

    Jiafan Ma, Kai Chen, Shunrong Li, Liling Zhu, Zhilong Pan, Qiang Liu & Erwei Song

  3. Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China

    Kai Chen & Zhuo Wu

  4. Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China

    Yunfang Yu, Yujie Tan & Zifan He

  5. Department of Breast Surgery, Tangshan People’s Hospital, Tangshan, 063001, Hebei, China

    Jingwu Li & Jie Ma

  6. Department of Breast Surgery, Tungwah Hospital, Sun Yat-sen University, Dongguan, 523413, China

    Jie Ouyang

  7. Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China

    Zhuo Wu & Haiqing Liu

  8. Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China

    Haojiang Li

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  1. Jiafan Ma
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Corresponding authors

Correspondence to Haojiang Li, Qiang Liu or Erwei Song.

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Guarantor

The scientific guarantor of this publication is Erwei Song.

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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 of the authors has significant statistical expertise.

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The requirement for informed consent was waived and patient data are anonymized.

Ethical approval

The study was approved by the IRBs of the three included hospitals, respectively. Among them, the approval number of Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYMH) IRB is 2020-KY-028, the approval number of Sun Yat-sen University Cancer Center (SYSUCC) IRB is B2020-333-01, and the approval number of Tangshan People’s Hospital (TSPH) IRB is RMYY-LLKS-2020-104.

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• multicenter study

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Ma, J., Chen, K., Li, S. et al. MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study. Eur Radiol 34, 1774–1789 (2024). https://doi.org/10.1007/s00330-023-10144-x

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