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• W4310520574 abstract "Abstract Purpose To develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer (BC) patients. Materials and Methods We retrospectively enrolled 196 patients with non-specific invasive breast cancer confirmed by pathology, radiomics and deep learning features were extracted from unenhanced and biphasic (arterial and venous phase) contrast-enhanced computed tomography (CECT), and the non-linear support vector machine (SVM) was used to construct the radiomics signature and the deep learning signature respectively. Next, a DLRN was developed with independent predictors and evaluated the performance of models in terms of discrimination and clinical utility. Results Multivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and clinical n stage were independent predictors. The DLRN accurately predicted ALNM, yielded an area under the receiver operator characteristic curve of 0.893(95% confidence interval, 0.814–0.972) in the validation set, with good calibration. Decision curve analysis confirmed that the DLRN had higher clinical utility than other predictors. Conclusions The DLRN had good predictive value for ALNM in breast cancer patients and provide valuable information for individual treatment." @default.
• W4310520574 created "2022-12-11" @default.
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• W4310520574 date "2022-12-01" @default.
• W4310520574 modified "2023-09-30" @default.
• W4310520574 title "Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer" @default.
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• W4310520574 doi "https://doi.org/10.21203/rs.3.rs-2314823/v1" @default.
• W4310520574 hasPublicationYear "2022" @default.
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