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Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging

Published:March 10, 2021DOI:https://doi.org/10.1016/j.acra.2021.02.008

      Rationale and Objectives

      Intra-peritumoural textural transition (Ipris) is a new radiomics method, which includes a series of quantitative measurements of the image features that represent the differences between the inside and outside of the tumour. This study aimed to explore the feasibility of Ipris analysis for the preoperative prediction of axillary lymph node (ALN) status in patients with breast cancer based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

      Materials and Methods

      This study was approved by the Institutional Review Board (IRB) of our hospital. One hundred sixty-six patients with clinicopathologically confirmed invasive breast cancer and ALN status were enrolled. All patients underwent preoperative breast DCE-MRI examinations. The primary breast lesion was manually segmented using the ITK-SNAP software for each patient. Two sets of image features were extracted, including Ipris features and conventional intratumoural features. Feature selection was conducted using Spearman correlation analysis and support vector machine with recursive feature elimination (SVM-RFE). Next, three models were established in training dataset: Model 1 was established by Ipris features; Model 2 was established by intratumoural features; Model 3 was established by combining Ipris features and intratumoural features. The performances of the three models were evaluated for the prediction of ALN status in testing datasets.

      Results

      Model 1 with four Ipris features achieved an AUC of 0.816 (95% CI, 0.733–0.883) and 0.829 (95% CI, 0.695–0.922) in the training and testing datasets, respectively. Model 2 with six intratumoural features achieved an AUC of 0.801 (95% CI, 0.716–0.870) and 0.824 (95% CI, 0.689–0.918) in the training and testing datasets, respectively. By incorporating the Ipris and intratumoural features, the AUC of Model 3 increased to 0.968 (95% CI, 0.916–0.992) and 0.855 (95% CI, 0.724–0.939) in the training and testing datasets, respectively.

      Conclusion

      Ipris features based on DCE-MRI can be used to predict ALN status in patients with breast cancer. The model combining intratumoural and Ipris features achieved higher prediction performance.

      KEY WORDS

      Abbreviations:

      ALN (axillary lymph node), DCE-MRI (dynamic contrast-enhanced magnetic resonance imaging), T2WI (T2-weighted imaging), IVIM (intravoxel incoherent motion), ADC (apparent diffusion coefficient), CT (computed tomography), SVM (support vector machine), SVM-RFE (support vector machine with recursive feature elimination), AUC (area under the receiver operating characteristic curve), ROI (region of interest), SLN (sentinel lymph node), ER (oestrogen receptor), PR (progesterone receptor), HER2 (human epidermal growth factor receptor 2), ROC (receiver operating characteristic), CI (confidence interval), GD (gradient difference), ID (intensity difference), GS (gradient sharpness), GE (gradient entropy)
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