Dynamic contrast-enhanced MRI radiomic features predict axillary lymph node metastasis of breast cancer

Yanna Shan; Xiangyang Gong; Zhongxiang Ding; Qijun Shen; Wen XU; Peipei Pang; Wei Wang

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Dynamic contrast-enhanced MRI radiomic features predict axillary lymph node metastasis of breast cancer

VernacularTitle: 动态增强MRI影像组学特征预测乳腺癌腋窝淋巴结转移的价值
Author: Yanna SHAN ¹ ; Xiangyang GONG ² ; Zhongxiang DING ³ ; Qijun SHEN ³ ; Wen XU ³ ; Peipei PANG ⁴ ; Wei WANG ⁵
Author Information

1. Department of Radiology, Affiliated Sir Run Run Shao Hospital of Zhejiang University School of Medicine, Hangzhou 310016, China (Shan Yanna Now Works in the Department of Radiology, Affiliated Hangzhou First People′s Hospital of Zhejiang University School of Medicine, Hangzhou 310006, China)
2. Department of Radiology, Affiliated Sir Run Run Shao Hospital of Zhejiang University School of Medicine, Hangzhou 310016, China (Gong Xiangyang Now Works in the Department of Radiology, People′s Hospital of Zhejiang,Hangzhou 310004,China)
3. Department of Radiology, Affiliated Hangzhou First People′s Hospital of Zhejiang University School of Medicine, Hangzhou 310006, China
4. GE China Ministry of Medical Life Sciences, Hangzhou 310000, China
5. Department of Ultrasound, Affiliated Hangzhou First People′s Hospital of Zhejiang University School of Medicine, Hangzhou 310006, China
Publication Type:Journal Article
Keywords: Breast neoplasms; Axillary lymph nodes; Magnetic resonance imaging; Radiomics; Heterogeneity
From: Chinese Journal of Radiology 2019;53(9):742-747
CountryChina
Language:Chinese
Abstract: Objective:To investigate the prognostic value of radiomics analysis in predicting axillary lymph nodes (ALN) metastasis of breast cancer based on dynamic contrast-enhanced MR imaging (DCE-MRI).
Methods:One hundred and ninety-six patients with suspected breast cancer were prospectively collected for dynamic breast DCE-MRI. Enhanced MR imaging data of 72 axillary lymph nodes were evaluated separately by a chief radiologist and a resident, and the consistency analysis was performed. Lymph nodes were dichotomized according to the pathology results derived from operation or biopsy under real-time virtual sonography based on MRI data. Clinical and imaging data were also divided into corresponding groups. (Imaging) Data from both groups were respectively classified as training set and testing set by stratified sampling in proportion with 3∶1. AK software was applied to extract 6 major categories of 385 features (including histogram, morphology, texture parameters, gray level co-occurrence matrix, run-length matrix and grey level zone size matrix from imaging), and a set of statistically significant features were subsequently obtained by dimension reduction. The prediction model was established through binary classification logistic regression and employed to externally test the validation set by the method of confusion matrix. Meanwhile, ROC analysis was applied to assess the diagnostic performance of the model.
Results:Of the 72 axillary lymph nodes, 35 were metastatic negative and 37 were positive. The consistency of enhanced MRI radiomics features was good, between 0.841 and 0.980. Uniformity, ClusterProminence_AllDirection_offset1_SD, Correlation_AllDirection_offset1, LongRunEmphasis_angle90_offset7 and SurfaceVolumeRatio were statistically significant differences (P<0.01), the area under the ROC between 0.747 and 0.931. In the training and testing group, the areas under the ROC, sensitivity, specificity and accuracy of the model were 0.953, 0.893, 0.926, 92.6% (50/54) and 0.944, 0.900, 1.000, 88.9% (16/18) respectively.
Conclusion:The prediction model based on radiomic features may provide a non-invasive and effective approach to the assessment of the risk of ALN metastasis of breast cancer.