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• Clinical Article •
Value of multimodal radiomics nomogram in predicting axillary lymph node metastasis in invasive ductal carcinoma of the breast before surgery
ZHANG Shuni  ZHAO Nannan  LI Yang  ZHU Yun  YANG Jingru  ZHANG Aoqi  GU Yihong  XIE Zongyu 

Cite this article as: ZHANG S N, ZHAO N N, LI Y, et al. Value of multimodal radiomics nomogram in predicting axillary lymph node metastasis in invasive ductal carcinoma of the breast before surgery[J]. Chin J Magn Reson Imaging, 2024, 15(4): 78-87. DOI:10.12015/issn.1674-8034.2024.04.013.


[Abstract] Objective To investigate the value of multimodal radiomics nomogram in predicting axillary lymph node (ALN) metastasis in invasive ductal carcinoma of the breast before surgery.Materials and Methods The clinical and imaging data of 224 patients with invasive ductal carcinoma of the breast confirmed by surgical pathology in our hospital from January 2019 to June 2023 were retrospectively collected. Firstly, the maximum level of the lesion of the T2WI image and the second phase of dynamic contrast-enhanced MRI (DCE-MRI) and the mammography (MG) of the same lesion were selected to delineate the region of interest, and the characteristics of the lesion area of interest were extracted. According to the ratio of 7∶3, the samples were randomly divided into 156 cases in the training set and 68 cases in the test set, and the feature dimensionality reduction screening was carried out by least absolute shrinkage and selection operator (LASSO) regression, 5 kinds of machine learning classifiers [support vector machine (SVM)、K nearest neighbors (KNN)、extreme gradient boosting (XGBoost)、logistic regression (LR)、randomforest (RF)] were selected to build a multimodal radiomics model, and the classifier with the best prediction performance was selected to establish MRI and mammography models. Univariate logistic regression was used to screen clinical high-risk factors and construct a clinical model. Finally, Radiomics score combined with clinical high-risk factors was selected to construct an electromics nomogram model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the efficacy of the model in predicting the ALN status of breast cancer patients, and the clinical practicability of the prediction model was evaluated by using the fitting ability of the calibration curve to evaluate the decision curve.Results Finally, 14 optimal radiomics features were obtained. The AUC value of the five machine learning classifiers in the test set ranged from 0.764-0.864, and the AUC value of SVM was the highest (0.864). Lymph node palpation (P<0.001) and MRI_ALN (P=0.005) were independent risk factors for ALN metastasis. The AUC, sensitivity, specificity and accuracy of the nomogram model training set were 0.941, 90.7%, 88.9% and 88.5%, respectively. The test sets were 0.926, 84.4%, 86.1%, and 85.3%, respectively.Conclusions The nomogram model has important value in predicting ALN status before surgery, and can assist in the formulation of scientific and effective clinical diagnosis and treatment plans.
[Keywords] breast cancer；invasive ductal carcinoma；axillary lymph nodes；radiomics；nomograms；mammography；magnetic resonance imaging

ZHANG Shuni1, 2   ZHAO Nannan1, 2   LI Yang1, 2   ZHU Yun1   YANG Jingru1, 2   ZHANG Aoqi1, 2   GU Yihong1, 2   XIE Zongyu1*  

1 Department of Radiology, the First Affiliated Hospital of Bengbu Medical University, Bengbu 233004, China

2 Graduate School of Bengbu Medical University, Bengbu 233004, China

Corresponding author: XIE Z Y, E-mail: zongyuxie@sina.com

Conflicts of interest   None.

Received  2024-01-11
Accepted  2024-04-07
DOI: 10.12015/issn.1674-8034.2024.04.013
Cite this article as: ZHANG S N, ZHAO N N, LI Y, et al. Value of multimodal radiomics nomogram in predicting axillary lymph node metastasis in invasive ductal carcinoma of the breast before surgery[J]. Chin J Magn Reson Imaging, 2024, 15(4): 78-87. DOI:10.12015/issn.1674-8034.2024.04.013.

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