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- Title
Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.
- Authors
Zhang, Jieqiu; Yin, Wei; Yang, Lu; Yao, Xiaopeng
- Abstract
Purpose: This study aims to develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer 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 CT, and the non-linear support vector machine 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 and 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.
- Subjects
METASTATIC breast cancer; LYMPHATIC metastasis; RADIOMICS; DEEP learning; COMPUTED tomography; NOMOGRAPHY (Mathematics)
- Publication
Molecular Imaging & Biology, 2024, Vol 26, Issue 1, p90
- ISSN
1536-1632
- Publication type
Article
- DOI
10.1007/s11307-023-01839-0