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Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase

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Abstract

Objective

To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer.

Materials and methods

This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs: (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model.

Results

Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant.

Conclusion

The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer.

Key Points

• Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer.

• We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CI:

Confidence interval

DCE:

Dynamic contrast-enhanced

DCE_P6:

DCE_Phase 6

DWI:

Diffusion-weighted imaging

ER:

Estrogen receptor

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size-zone matrix

HER-2:

Human epidermal growth factor receptor-2

ICC:

Interobserver correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

NAC:

Neo-adjuvant chemotherapy

NGTDM:

Neighborhood gray tone difference matrix

PR:

Progesterone receptor

ROC:

Receiver operating characteristic

ROI:

Region of interest

T2WI:

T2-weighted imaging

TIL:

Tumour-infiltrating lymphocyte

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Acknowledgements

We gratefully acknowledge all the members of the Department of Radiology, Guangzhou First People’s Hospital.

Funding

This study has received funding from the National Natural Science Foundation of China (No. 81901711).

Author information

Author notes

  1. Wen-jie Tang and Qing-cong Kong contributed equally to this work.

Authors and Affiliations

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

    Wen-jie Tang, Zi-xuan Cheng, Zhe Jin, Lei-xin Chen, Wen-ke Hu, Ying-ying Liang, Xin-hua Wei, Yuan Guo & Xin-qing Jiang

  2. Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, Guangdong, China

    Qing-cong Kong

  3. Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China

    Yun-shi Liang

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  1. Wen-jie Tang
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Correspondence to Yuan Guo or Xin-qing Jiang.

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

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Tang, Wj., Kong, Qc., Cheng, Zx. et al. Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase. Eur Radiol 32, 864–875 (2022). https://doi.org/10.1007/s00330-021-08173-5

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