基于人工智能和超声影像组学的三阴性乳腺癌预后预测模型
Prognostic prediction model for triple-negative breast cancer using artificial intelligence and ultrasound radiomics.
摘要(中文译文)
开发并验证一种人工智能（AI）驱动的超声影像组学模型，用于预测接受标准化治疗的三阴性乳腺癌（TNBC）患者术后复发和转移。我们开展了一项回顾性研究，纳入668例女性TNBC患者（2013—2018年接受治疗）。首先采用单因素和多因素logistic回归筛选有意义的临床病理变量，用于基线评估并为模型开发提供依据。在肿瘤分割后，使用PyRadiomics从治疗前超声图像中自动提取影像组学特征。通过将LASSO用于特征选择与支持向量机（SVM）分类器相结合，构建影像组学标签。采用受试者工作特征曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）和混淆矩阵评估模型性能。该超声影像组学模型对任何复发/转移均显示出较高的预测准确性，训练队列AUC为0.9458，验证队列AUC为0.8983。在区分局部区域复发与远处转移方面，该模型在训练队列和验证队列中的AUC分别为0.9341和0.8824。校准曲线和决策曲线分析证实了该模型稳健的预测能力及潜在的临床实用性。本研究表明，AI增强的超声影像组学模型能够有效预测TNBC术后复发和转移模式，为支持个体化预后评估提供了一种有前景的无创工具。
Abstract
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. 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. 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. 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.
如何引用
AMA
Yushanjiang Zilalan, Jingjing Fan, Tong Sha, Wen Liu, Hongtao Li. Prognostic prediction model for triple-negative breast cancer using artificial intelligence and ultrasound radiomics.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1654953.
APA
Yushanjiang Zilalan, Jingjing Fan, Tong Sha, Wen Liu, Hongtao Li (2026). Prognostic prediction model for triple-negative breast cancer using artificial intelligence and ultrasound radiomics.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1654953
GB/T 7714
Yushanjiang Zilalan, Jingjing Fan, Tong Sha, Wen Liu, Hongtao Li. Prognostic prediction model for triple-negative breast cancer using artificial intelligence and ultrasound radiomics.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1654953.
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