Rationale and Objectives
In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered
an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics
models for the prediction of LVI status in patients with BC, using preoperative MRI
images.
Materials and Methods
This retrospective study included patients with BC with known LVI status and preoperative
MRI. The dataset was split into training and unseen testing sets by stratified sampling
with a 2:1 ratio. 2D and 3D radiomic features were extracted from contrast-enhanced
T1 weighted images (C+T1W) and apparent diffusion coefficient (ADC) maps. The reliability
of the features was assessed with two radiologists' segmentation data. Dimension reduction
was done with reliability analysis, multi-collinearity analysis, removal of low-variance
features, and feature selection. ML models were created with base, tuned, and boosted
random forest algorithms.
Result
A total of 128 lesions (LVI-positive, 76; LVI-negative, 52) were included. The best
model performance was achieved with tunning and boosting model based on 3D ADC maps
and selected four radiomic features. The area under the curve and accuracy were 0.726
and 63.5% in the training data, 0.732 and 76.7% in the test data, respectively. The
overall sensitivity and positive predictive values were 68% and 69.6% in the training
data, 84.6% and 78.6% in the test data, respectively.
Conclusion
: ML and radiomics based on 3D segmentation of ADC maps can be used to predict LVI
status in BC, with satisfying performance.
Key Words
Abbreviations:
ADC (Apparent diffusion coefficient), AUC (Area under the curve), BC (Breast cancer), C+T1W (Contrast-enhanced T1 weighted), CV (Cross-validation), DWI (Diffusion-weighted imaging), LVI (Lymphovascular invasion), ML (Machine learning), MRI (Magnetic resonance imaging), NAC (Neoadjuvant chemotherapy), PPV (Positive predictive value), RF (Random Forest), 2D (Two dimensional), 3D (Three dimensional)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: December 04, 2021
Accepted:
October 27,
2021
Received in revised form:
October 19,
2021
Received:
September 23,
2021
Identification
Copyright
© 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
