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September 10, 2025Journal of Ultrasound in Medicine

Prognostic Utility of a Deep Learning Radiomics Nomogram Integrating Ultrasound and Multi‐Sequence MRI in Triple‐Negative Breast Cancer Treated with Neoadjuvant Chemotherapy

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Authors

CCCheng ChenPXPeng XiaoKSKai Sang

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Overview

Retrospective analysis shows strong predictive capabilities for recurrence and metastasis in TNBC patients, suggesting enhanced risk stratification through deep learning radiomics.

Key Points

  • The DLRN model shows strong predictive capabilities for disease-free survival in TNBC patients undergoing NAC.
  • C-index values for DFS were between 0.859 and 0.887, indicating high accuracy in predicting patient outcomes.
  • Risk stratification demonstrated significant differences in recurrence rates between low-risk and high-risk groups in the study population.
  • The integration of clinical parameters with radiomics features may facilitate individualized prognostic evaluations.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68c1956b9b7b07f3a06198f6https://doi.org/10.1002/jum.70054
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