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Radiomics nomogram for predicting axillary lymph node metastasis—a potential method to address the limitation of axilla coverage in cone-beam breast CT: a bi-center retrospective study

  • Breast Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

Cone-beam breast CT (CBBCT) has an inherent limitation that the axilla cannot be imaged in its entirety. We aimed to develop and validate a nomogram based on clinical factors and contrast-enhanced (CE) CBBCT radiomics features to predict axillary lymph node (ALN) metastasis and complement limited axilla coverage.

Material and methods

This retrospective study included 312 patients with breast cancer from two hospitals who underwent CE-CBBCT examination in a clinical trial (NCT01792999) during 2012–2020. Patients from TCIH comprised training set (n = 176) and validation set (n = 43), and patients from SYSUCC comprised external test set (n = 93). 3D ROIs were delineated manually and radiomics features were extracted by 3D Slicer software. RadScore was calculated and radiomics model was constructed after feature selection. Clinical model was built on independent predictors. Nomogram was developed with independent clinical predictors and RadScore. Diagnostic performance was compared among three models by ROC curve, and decision curve analysis (DCA) was used to evaluate the clinical utility of nomogram.

Results

A total of 139 patients were ALN positive and 173 patients were negative. Twelve radiomics features remained after feature selection. Location and focality were selected as independent predictors for ALN status. The AUC of nomogram in external test set was higher than that of clinical model (0.80 vs. 0.66, p = 0.012). DCA demonstrated that the nomogram had higher overall net benefit than that of clinical model.

Conclusion

The nomogram combined CE-CBBCT-based radiomics features and clinical factors could have potential in distinguishing ALN positive from negative and addressing the limitation of axilla coverage in CBBCT.

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Abbreviations

ALN:

Axillary lymph node

ALND:

Axillary lymph node dissection

ANOVA:

Analysis of variance

AUC:

Area under the curve

BI-RADS:

Breast Imaging Reporting and Data System

BPE:

Background parenchymal enhancement

CBBCT:

Cone-beam breast CT

CESM:

Contrast-enhanced spectral mammography

CI:

Confidence interval

DCA:

Decision curve analysis

FGT:

Fibroglandular tissue

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage selection operator

MLR:

Multivariable logistic regression

NME:

Non-mass enhancement

RadScore:

Radiomics score

ROC:

Receiver operating characteristic

ROI:

Region of interest

SLNB:

Sentinel lymph node biopsy

UIQ:

Upper inner quadrant

VIF:

Variance inflation factor

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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).

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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, Yuwei Zhang, Aidi Liu, Yafei Wang, Mengran Zhao, Haijie Li & Zhaoxiang Ye

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

    Yueqiang Zhu

  3. 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

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  1. Yueqiang Zhu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YZ, YM, YZ, AL, YW, MZ, HL, NH, YW, and ZY. The first draft of the manuscript was written by YZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhaoxiang Ye.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Tianjin Medical University Cancer Institute and Hospital (E2012036) and Sun Yat-Sen University Cancer Center (A2011-030-01).

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Informed consent was obtained from all individual participants included in the study.

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Zhu, Y., Ma, Y., Zhang, Y. et al. Radiomics nomogram for predicting axillary lymph node metastasis—a potential method to address the limitation of axilla coverage in cone-beam breast CT: a bi-center retrospective study. Radiol med 128, 1472–1482 (2023). https://doi.org/10.1007/s11547-023-01731-5

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