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)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: March 10, 2021
Accepted:
February 8,
2021
Received in revised form:
January 25,
2021
Received:
November 30,
2020
Identification
Copyright
© 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
