Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Abstract

BackgroundThe use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.PurposeThe aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.MethodsWe retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).ResultsThe radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.ConclusionThe radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

“…Only one ( 10 ) used prospectively collected data. Furthermore, the studies applied radiomics based on different imaging modalities: MRI, 17 ( 3 , 11 , 22 , 23 , 28 – 39 ); ultrasound, 7 ( 14 , 24 , 25 , 27 , 40 – 42 ); CT, 2 ( 12 , 43 ); PET–CT, 2 ( 10 , 44 ); and MMG, 3 ( 13 , 26 , 45 ).…”

The Value of Shear Wave Elastography in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis and Its Correlation With Molecular Classification of Breast Masses

“…Therefore, studies with a larger sample size involving multiple units should be considered. Plus, we tried to employ SWE to predict the molecular type of breast cancer with the elastic modulus values ( 58 , 59 ). Unfortunately, no significant difference was found ​​between the six different groups.…”

Clinical prediction model based on 18F-FDG PET/CT plus contrast-enhanced MRI for axillary lymph node macrometastasis

“…Recent clinical prediction models utilizing the pretreatment radiomics features of contrast-enhanced MRI and contrastenhanced CT also exhibited a high diagnostic accuracy for ALN metastasis in patients with breast cancer, with an AUC of approximately 0.900 in the validation cohort (27)(28)(29). In radiomics studies, prediction models are constructed using machine learning to improve the predictive ability, making it a promising method to analyze the morphological features of ALNs for better preoperative nodal staging prediction.…”