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A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer

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Abstract

Objective

The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).

Methods

A total of 100 consecutive IDC patients who underwent surgical resection of primary tumor with sentinel lymph-node biopsy and/or ALN dissection without any neoadjuvant treatment were analyzed. Volume of interests (VOIs) were drawn more than 2.5 of standardized uptake value in the primary tumor on the PET scan using 3D slicer. Pyradiomics package was used for the extraction of texture features in python. The radiomics prediction model for ALN metastasis was developed in 75 patients of the training cohort and validated in 25 patients of the test cohort. XGBoost algorithm was utilized to select features and build radiomics model. The sensitivity, specificity, and accuracy of the predictive model were calculated.

Results

ALN metastasis was found in 43 patients (43%). The sensitivity, specificity, and accuracy of F-18 FDG PET/CT for the diagnosis of ALN metastasis in the entire patients were 55.8%, 93%, and 77%, respectively. The radiomics model for the prediction of ALN metastasis was successfully developed. The sensitivity, specificity, and accuracy of the radiomics model for the prediction of ALN metastasis in the test cohorts were 90.9%, 71.4%, and 80%, respectively.

Conclusion

The machine learning-based radiomics model showed good sensitivity for the prediction of ALN metastasis and could assist the preoperative individualized prediction of ALN status in patients with IDC.

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Funding

This research was supported by the Bisa Research Grant of Keimyung University in 2019.

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Authors and Affiliations

  1. Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, Republic of Korea

    Bong-Il Song

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Correspondence to Bong-Il Song.

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Song, BI. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer 28, 664–671 (2021). https://doi.org/10.1007/s12282-020-01202-z

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  • DOI: https://doi.org/10.1007/s12282-020-01202-z

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