ORIGINAL RESEARCH article
Front. Oncol.
Sec. Breast Cancer
This article is part of the Research TopicAI-Powered Insights: Predicting Treatment Response and Prognosis in Breast CancerView all 18 articles
Prognostic Prediction Model for Triple-Negative Breast Cancer Using Artificial Intelligence and Ultrasound Radiomics
Provisionally accepted
- 1Tumor Hospital of Xinjiang Medical University, Ürümqi, China
- 2Cancer Hospital of Xinjiang Medical University, Urumqi, China
- 3Xinjiang Institute of Engineering, Urumqi, China
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Objective: To develop and validate an artificial intelligence (AI)-driven ultrasound radiomics model for predicting postoperative recurrence and metastasis in patients with triple-negative breast cancer (TNBC) receiving standardized therapies.Methods: We conducted a retrospective study of 668 female TNBC patients (treated 2013-2018). Univariate and multivariate logistic regression were first used to screen significant clinicopathological variables for baseline assessment and to inform model development. Radiomic features were automatically extracted from pretreatment ultrasound images using PyRadiomics following tumor segmentation. A radiomics signature was constructed by integrating LASSO for feature selection with a support vector machine (SVM) classifier. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and confusion matrices.Results: The ultrasound radiomics model showed high predictive accuracy for any recurrence/metastasis, with an AUC of 0.9458 in the training cohort and 0.8983 in the validation cohort. For distinguishing between locoregional recurrence and distant metastasis, the model achieved AUCs of 0.9341 and 0.8824 in the training and validation cohorts, respectively. Calibration and decision curve analyses confirmed the model's robust predictive capability and potential clinical utility.Conclusion: This study demonstrates that an AI-enhanced ultrasound radiomics model can effectively predict postoperative recurrence and metastasis patterns in TNBC, offering a promising non-invasive tool to support personalized prognosis assessment.
Keywords: artificial intelligence, Metastasis-recurrence risk, Prognostic prediction, Triple-negative breast cancer, ultrasound radiomics
Received: 27 Jun 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Zilalan, Fan, Sha, Liu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Wen Liu
Hongtao Li
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