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Value of DCE-MRI based radiomics features for prediction of axillary lymph node metastasis in breast carcinoma
WANG Yunxia  SHANG Yiyan  GUO Yaxin  HAI Menglu  Gao Yang  WEI Huanhuan  LI Xiaodong  WANG Meiyun  TAN Hongna 

Cite this article as: WANG Y X, SHANG Y Y, GUO Y X, et al. Value of DCE-MRI based radiomics features for prediction of axillary lymph node metastasis in breast carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(3): 21-27. DOI:10.12015/issn.1674-8034.2023.03.005.


[Abstract] Objective To explore the value of radiomics features extracted from dynamic contrast enhanced MRI (DCE-MRI) for preoperative prediction of axillary lymph node (ALN) metastasis in breast cancer.Materials and Methods The first preoperative MRI images and clinicopathological data (including patient age, location and size of lesion, SBR grade, expression of ER, PR, HER-2 and Ki-67, whether ALN metastases and vascular cancer thrombus were present) of patients with breast cancer confirmed by surgical pathology in Henan Provincial People's Hospital from January 2017 to December 2020 were retrospectively analyzed. A total of 356 patients aged 26 to 82 (49.17±10.75) years were enrolled, which were randomly divided into the training set (n=284) and test set (n=72) according to the ratio of 8∶2. The radiomics features of phase 3 images in the DCE-T1WI sequence were extracted, and the quantitative radiomics features having strong correlation with ALN metastasis were selected using Mann-Whitney U test, Z-score normalization, variance threshold, K-best and least absolute shrinkage and selection operator (LASSO) regression methods. A variety of classifier algorithms were used to construct radiomics labels in a permutation-combination way. The area under the curve (AUC), sensitivity, specificity and accuracy of receiver operating characteristic curve (ROC) were used to evaluate the efficiency of the model, then the optimal prediction model was selected according to the efficiency.Results Among 356 patients with breast cancer, 117 patients (32.9%, 117/356) had ALN metastasis and 239 patients (67.1%, 239/356) had no ALN metastasis. There was a statistically significant difference in HER-2 positive expression between the ALN metastasis group and non-metastasis group (χ2=5.433, P=0.020), and there was no statistically significant differences in the other clinicopathological indicators between the two groups (P＞0.05).There was no statistically significant differences in clinicopathological indicators between the training set and the test set (P＞0.05). A total of 18 radiomics features having strong correlation with ALN metastasis were selected finally from the initial 643 radiomics features, including each 6 morphological features, first order features and texture features respectively. The optimal ALN prediction model was selected through radiomics signatures based on maximum absolute value normalization and Bagging decision tree algorithm, and the AUC value, sensitivity, specificity and accuracy of the model in the training set and test set were 0.929 [95% confidence interval (CI): 0.897-0.960], 69.9%, 96.9%, 88.0% and 0.803 (95% CI: 0.701-0.905), 75.0%, 75.0% and 75.0% respectively.Conclusions The prediction model based on DCE-MRI radiomics features could be helpful for preoperative predicting ALN metastasis in breast cancer.
[Keywords] breast cancer；axillary lymph node metastasis；predictive effectiveness；radiomics；magnetic resonance imaging

WANG Yunxia1   SHANG Yiyan1   GUO Yaxin2   HAI Menglu3   Gao Yang4   WEI Huanhuan5   LI Xiaodong1   WANG Meiyun1   TAN Hongna1*  

1 Department of Medical Imaging, People's Hospital of Henan University (Henan Provincial People's Hospital), Zhengzhou 450003, China

2 Department of Medical Imaging, People's Hospital of Zhengzhou University (Henan Provincial People's Hospital), Zhengzhou 450003, China

3 Department of Medical Imaging, Affiliated Cancer Hospital of Zhengzhou University (Henan Provincial Cancer Hospital), Zhengzhou 450008, China

4 Heart Center, People's Hospital of Zhengzhou University (Henan Provincial People's Hospital), Zhengzhou 450003, China

5 Academy of Medical Science, Zhengzhou University, Zhengzhou 450000, China

Corresponding author: Tan HN, E-mail: natan2000@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Henan Province (No. 202300410081); Medical Science and Technological Project of Henan Province (No. LHGJ20220055).
Received  2022-12-12
Accepted  2023-03-06
DOI: 10.12015/issn.1674-8034.2023.03.005
Cite this article as: WANG Y X, SHANG Y Y, GUO Y X, et al. Value of DCE-MRI based radiomics features for prediction of axillary lymph node metastasis in breast carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(3): 21-27. DOI:10.12015/issn.1674-8034.2023.03.005.

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