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Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study

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A Commentary to this article was published on 06 November 2023

Abstract

Objectives

To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer.

Methods

Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics. After feature selection, support vector machine models were developed to predict ALN status and metastatic burden. To avoid overfitting on a specific patient subset, 100 randomly stratified splits were made to assign the patients to either training/fine-tuning or test set. Area under the receiver operating characteristic curve (AUC) of these radiomics models was compared to those obtained when training the models only with clinical features and combined clinical-radiomics descriptors. Ground truth was established by histopathology.

Results

One hundred and eighteen patients had ALN metastasis (N + (≥ 1)). Of these, 74 had low burden (N + (1~2)) and 44 high burden (N + (≥ 3)). The remaining 156 patients had none (N0). AUC values across the 100 test repeats in predicting ALN status (N0/N + (≥ 1)) were 0.75 ± 0.05 (0.67~0.93, radiomics model), 0.68 ± 0.07 (0.53~0.85, clinical model), and 0.74 ± 0.05 (0.67~0.88, combined model). For metastatic burden prediction (N + (1~2)/N + (≥ 3)), AUC values were 0.65 ± 0.10 (0.50~0.88, radiomics model), 0.55 ± 0.10 (0.40~0.80, clinical model), and 0.64 ± 0.09 (0.50~0.90, combined model), with all the ranges spanning 0.5. In both cases, the radiomics model was significantly better than the clinical model (both p < 0.01) and comparable with the combined model (p = 0.56 and 0.64).

Conclusions

Radiomics features of primary tumors could have potential in predicting ALN metastasis in CE-CBBCT imaging.

Clinical relevance statement

The findings support potential clinical use of radiomics for predicting axillary lymph node metastasis in breast cancer patients and addressing the limited axilla coverage of cone-beam breast CT.

Key Points

• Contrast-enhanced cone-beam breast CT-based radiomics could have potential to predict N0 vs. N + (≥ 1) and, to a limited extent, N + (1~2) vs. N + (≥ 3) from primary tumor, and this could help address the limited axilla coverage, pending future verifications on larger cohorts.

• The average AUC of radiomics and combined models was significantly higher than that of clinical models but showed no significant difference between themselves.

• Radiomics features descriptive of tumor texture were found informative on axillary lymph node status, highlighting a higher heterogeneity for tumor with positive axillary lymph node.

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Abbreviations

ACOSOG:

American College of Surgeons Oncology Group

ALN:

Axillary lymph node

ALND:

Axillary lymph node dissection

AUC:

Area under the curve

CE-CBBCT:

Contrast-enhanced cone-beam breast CT

FNB:

Fine-needle biopsy

ICC:

Intraclass correlation coefficient

IQR:

Interquartile range

LoG:

Laplacian of Gaussian

LASSO:

Least absolute shrinkage selection operator

NME:

Non-mass enhancement

ROC:

Receiver operating characteristic

SLNB:

Sentinel lymph node biopsy

SVM:

Support vector machine

VOI:

Volume of interest

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Funding

This study was supported by National Key R&D Program of China (No. 2021YFC2500400, 2021YFC2500402, 2017YFC0112600, 2017YFC0112601, 2017YFC0112605), National Natural Science Foundation of China (No. 81571671), Tianjin Science and Technology Major Project (No. 19ZXDBSY00080), Key Project of Tianjin Medical Industry (No. 16KG130), Tianjin Medical University Cancer Institute and Hospital Fund (B2118, B2219), and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A). This study was also supported in part by the National Cancer Institute (NCI) of the National Institutes of Health (NIH) (No. R01CA181171). The content is solely the responsibility of the authors and does not represent the official views of the NCI or the NIH.

Author information

Authors and Affiliations

  1. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin’s Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China

    Yueqiang Zhu, Yue Ma, Aidi Liu, Yafei Wang, Yuwei Zhang, Haijie Li, Mengran Zhao, Peng Han, Lu Yin & Zhaoxiang Ye

  2. Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands

    Yueqiang Zhu, Ioannis Sechopoulos & Marco Caballo

  3. Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Mei-Hua-Dong Road, Xiangzhou District, Zhuhai, 519000, China

    Zhenzhen Zhai

  4. Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China

    Ni He & Yaopan Wu

  5. Dutch Expert Center for Screening (LRCB), PO Box 6873, Nijmegen, 6503 GJ, The Netherlands

    Ioannis Sechopoulos

  6. Technical Medicine Centre, University of Twente, PO Box 217, Enschede, 7500 AE, The Netherlands

    Ioannis Sechopoulos

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  1. Yueqiang Zhu
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  2. Yue Ma
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  3. Zhenzhen Zhai
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  8. Mengran Zhao
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  9. Peng Han
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  10. Lu Yin
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  11. Ni He
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  13. Ioannis Sechopoulos
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  14. Zhaoxiang Ye
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  15. Marco Caballo
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Corresponding author

Correspondence to Zhaoxiang Ye.

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Guarantor

The scientific guarantor of this publication is Zhaoxiang Ye.

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

One of the authors (Marco Caballo) has significant statistical expertise.

Informed Consent

Data for this study was collected from two prospective clinical trials, named “Koning breast CT for breast imaging in China” and “The technical operations and standard clinical application protocol of cone-beam breast CT in diagnostic process of breast cancer”, in which written informed consent including permission to re-use the data for any further retrospective analysis was obtained from every patient at the time of enrollment.

Ethical Approval

Institutional Review Board approval was obtained (E2012036, bc2016039, and A2011-030-01).

Study subjects or cohorts overlap

The study subjects were collected from two prospective clinical trials (NCT01792999 and NCT03861221), aiming to assess the performance of new-generation breast imaging modality—CBBCT and explore the clinical application guideline of CBBCT, respectively. There have been several publications that share the same cohort with the current study regarding breast coverage and patient comfort comparison, diagnostic performance analysis, visual and quantitative breast density assessment, molecular subtyping, tumor size evaluation, and BI-RADS atlas exploration. In contrast, here we investigated axillary lymph node status and metastatic burden prediction that has not been evaluated previously.

• Li H, Yin L, Ye Z et al (2015) Comparative study of breast tissue coverage in cone-beam breast CT versus digital mammography. Chin J Radiol 49:488-490 in Chinese

• He N, Wu YP, Kong Y et al (2016) The utility of breast cone-beam computed tomography, ultrasound, and digital mammography for detecting malignant breast tumors: a prospective study with 212 patients. Eur J Radiol 85:392-403

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• Ma Y, Cao Y, Liu A et al (2019) A reliability comparison of cone-beam breast computed tomography and mammography: breast density assessment referring to the fifth edition of the BI-RADS atlas. Acad Radiol 26:752-759

• Li H, Yin L, He N et al (2019) Comparison of comfort between cone-beam breast computed tomography and digital mammography. Eur J Radiol 120:108674

• Zhu Y, Zhang Y, Ma Y et al (2020) Cone-beam breast CT features associated with HER2/neu overexpression in patients with primary breast cancer. Eur Radiol 30:2731-2739

• Ma Y, Liu A, O’Connell AM et al (2021) Contrast-enhanced cone-beam breast CT features of breast cancers: correlation with immunohistochemical receptors and molecular subtypes. Eur Radiol 31:2580-2589

• Wang Y, Ma Y, Zhu Y et al (2021) Value of cone-beam breast CT in differentiating benign from malignant dense breast masses. Chin J Radiol 55:961-967 in Chinese

• Zhang Y, Ma Y, Li Y et al (2021) Comparative study of cone-beam breast CT and breast MRI in diagnosis of BI-RADS 4 lesions on mammography or ultrasound. J Clin Radiol 40:1703-1708 in Chinese

• Zhu Y, O'Connell AM, Ma Y et al (2022) Dedicated breast CT: state of the art-part I. historical evolution and technical aspects. Eur Radiol 32:1579-1589

• Zhu Y, O'Connell AM, Ma Y et al (2022) Dedicated breast CT: state of the art-part II. clinical application and future outlook. Eur Radiol 32:2286-2300

• Liu A, Yin L, Ma Y et al (2022) Quantitative breast density measurement based on three-dimensional images: a study on cone-beam breast computed tomography. Acta Radiol 63:1023-1031

• Ma Y, Liu A, Zhang Y et al (2022) Comparison of background parenchymal enhancement (BPE) on contrast-enhanced cone-beam breast CT (CE-CBBCT) and breast MRI. Eur Radiol 32:5773-5782

• Liu A, Ma Y, Yin L et al (2023) Comparison of malignant calcification identification between breast cone-beam computed tomography and digital mammography. Acta Radiol 64:962-970

• Wang Y, Zhao M, Ma Y et al (2023) Accuracy of preoperative contrast-enhanced cone-beam breast CT in assessment of residual tumor after neoadjuvant chemotherapy: a comparative study with breast MRI. Acad Radiol 30:1805-1815

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Zhu, Y., Ma, Y., Zhai, Z. et al. Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study. Eur Radiol 34, 2576–2589 (2024). https://doi.org/10.1007/s00330-023-10256-4

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