Prognostic Utility of a Deep Learning Radiomics Nomogram Integrating Ultrasound and Multi-Sequence MRI in Triple-Negative Breast Cancer Treated with Neoadjuvant Chemotherapy.
Authors
Affiliations (6)
Affiliations (6)
- Department of Ultrasound, Lianyungang Traditional Chinese Medicine Hospital, Lianyungang, China.
- Department of Ultrasound, Donghai Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
- Department of Ultrasound, The First People's Hospital of Lianyungang, Lianyungang, China.
- Department of Ultrasound, Lianyungang Municipal Oriental Hospital, Lianyungang, China.
- Ultrasound Department, Lianyungang Maternal and Children's Health Care Hospital, Lianyungang, China.
- Department of Medical Imaging, The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, China.
Abstract
The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics (DLRN) features derived from ultrasound and multi-sequence magnetic resonance imaging (MRI) for predicting survival, recurrence, and metastasis in patients diagnosed with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC). This retrospective, multicenter study included 103 patients with histopathologically confirmed TNBC across four institutions. The training group comprised 72 cases from the First People's Hospital of Lianyungang, while the validation group included 31 cases from three external centers. Clinical and follow-up data were collected to assess prognostic outcomes. Radiomics features were extracted from two-dimensional ultrasound and three-dimensional MRI images following image segmentation. A DLRN model was developed, and its prognostic performance was evaluated using the concordance index (C-index) in comparison with alternative modeling approaches. Risk stratification for postoperative recurrence was subsequently performed, and recurrence and metastasis rates were compared between low- and high-risk groups. The DLRN model demonstrated strong predictive capability for DFS (C-index: 0.859-0.887) and moderate performance for overall survival (OS) (C-index: 0.800-0.811). For DFS prediction, the DLRN model outperformed other models, whereas its performance in predicting OS was slightly lower than that of the combined MRI + US radiomics model. The 3-year recurrence and metastasis rates were significantly lower in the low-risk group than in the high-risk group (21.43-35.71% vs 77.27-82.35%). The preoperative DLRN model, integrating ultrasound and multi-sequence MRI, shows promise as a prognostic tool for recurrence, metastasis, and survival outcomes in patients with TNBC undergoing NAC. The derived risk score may facilitate individualized prognostic evaluation and aid in preoperative risk stratification within clinical settings.