2743
Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer1Xinhua Hospital Affiliated to Shanghai Jiao Tong University School Of Medicine, shanghai, China
Synopsis
Keywords: Breast, Radiomics, Deep Learning
This study described the application of MRI-based deep learning radiomics in patients with breast cancer, presenting a novel individualized clinical decision nomogram that could be used to predict axillary lymph node metastasis providing a noninvasive approach to assist clinicians in clinical decision-making.Objective
To develop a radiomic signatures constructed from deep learning features and a radiomic nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients.Methods
Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions was studied. The included patients were divided into two cohorts by time (training/testing cohort, n=366/122). Deep learning features were extracted from diffusion-weighted imaging–quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI(DCE-MRI) by a pretrained Neural Networks of Densenet121. After the selection of both radiomic and clinicopathological features, deep learning signature and a nomogram were built for independent validation.Results
Twenty-three deep learning features were automatically selected in the training cohort to establish the deep learning signature of ALNM. Three clinicopathological factors, including LN palpability (odds ratio (OR)= 6.04; 95% confidence interval (CI)= 3.06-12.54, P=0.004), tumor size in MRI ( OR= 1.45, 95%CI= 1.18-1.80, P=0.104) and Ki-67( OR=1.01；95％CI= 1.00-1.02，P= 0.099), were selected and combined with radiomic signature to build a combined nomogram. The nomogram showed excellent predictive ability for ALNM (AUC 0.80 and 0.71 in training and testing cohorts, respectively). The sensitivity, specificity, and accuracy were 65%, 80%, and 75%, respectively in the testing cohort.Conclusions
This study described the application of MRI-based deep learning radiomics in patients with breast cancer, presenting a novel individualized clinical decision nomogram that could be used to predict ALNM providing a noninvasive approach to assist clinicians in clinical decision-making.Acknowledgements
This study was supported by National Nature Science Foundation of China (No. 82071870, No. 82101991), the Program of Shanghai Science and Technology Committee (No. 21S31905000,19DZ1930504). The funders had no role in study design, data collection and analysis, decision to publish, and preparation of the manuscript.
No potential conflicts of interest are disclosed by all authors.
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