A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features

Abstract

Background: The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status.

Materials and methods: Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model.

Results: Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature.

Conclusion: This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

Keywords: Axillary lymph node; Breast neoplasms; Breast specific gamma image; Machine learning; Ultrasonography.

Fig. 1

Working flow of this study

Fig. 2

An example of breast-specific gamma imaging analysis. (A) showed a left-sided breast cancer without axillary lymph node metastasis, the yellow rectangle showed the uptake of Technetium-99 m Sestamibi in breast. (B) showed a left-sided breast cancer with axillary lymph node metastasis, the red circle showed a positive axillary mass

Fig. 3

An example of ultrasonographic lymph node imagines analysis. (A) showed an ultrasound image of the right axilla exhibiting no signs of lymph node metastasis. (B) showed a color Doppler flow imaging (CDFI) image of the right axilla, indicating the absence of lymph node metastasis. (C) showed an ultrasound image of the left axilla showcasing lymph node metastasis, featuring a 24.0*16.0 mm mass identified as a lymph node. (D) showed a CDFI ultrasound image of lymph node metastasis in the left axilla, revealing discernible colored blood flow within the affected lymph node

Fig. 4

Model selection. (A) The line graph shows the predictive values of each model in test set. (B-G) The relative weights of selected variables in each model. GLM: generalized linear model; RF: random forest; SVM: support vector machine; NNET: neural network; GBM: gradient boosting machine; XGB: extreme boosting machine; RI: resistance index; CDFI: the color Doppler flow imaging grade; TNR: tumour-to-normal lesion ratio; MLO: mediolateral oblique; BSGI: breast specific gamma image; US: Ultrasonography

Fig. 5

An online calculator for predcting ALN metastasis. SVM: support vector machine; ALN: axillary lymph node; CDFI: the CDFI grade of lymph node; BSGI: breast-specific gamma imaging

Fig. 6

Evaluation of the model effectiveness with and without the BSGI features by ROC curves. US: Ultrasonography; BSGI: breast-specific gamma imaging; AUC: area under the curve