Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI
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To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T2-weighted fat suppression (T2-FS) and diffusion-weighted MRI (DWI).
We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T2-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1–10) based on different combination of image features using stepwise forward method.
For T2-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T2-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.
Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.
• SLN biopsy to access breast cancer metastasis has multiple complications.
• Radiomics uses features extracted from medical images to characterise intratumour heterogeneity.
• We combined T 2 -FS and DWI textural features to predict SLN metastasis non-invasively.
KeywordsImaging Breast cancer Sentinel lymph node metastasis Radiomics Preoperative prediction
Axillary lymph node
Area under the curve
Sentinel lymph node
T2-weighted fat suppression
Compliance with ethical standards
The scientific guarantor of this publication is Shuixing Zhang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
This study has received funding by the National Scientific Foundation of China (81571664), the Science and Technology Planning Project of Guangdong Province (2014A020212244, 2016A020216020, 2015B010131011) and The Science and Technology Project of Guangdong Province (2015B010131011).
Statistics and biometry
One of the authors has significant statistical expertise.
Written informed consent was waived by the institutional review board.
Institutional review board approval was obtained.
• diagnostic or prognostic study
• performed at one institution
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