许多预测模型已经开发出来，以支持针对乳腺癌患者的治疗决策。只有少数模型已进行外部验证，而这是在临床实践中实施所必需的。本研究旨在使用基于人口的荷兰数据对已发表的临床预测模型进行外部验证。来自荷兰癌症登记处（NCR）的患者、肿瘤和治疗相关数据被提取。使用接收器操作特性曲线下面积（AUC）、标度Brier分数和模型校准来评估模型的性能。通过决策曲线分析来评估适用风险阈值的净效益。在评估了922个模型后，有87个（9%）被包括在验证中。由于模型描述不完整（n=262（28%））、缺乏所需数据（n=521（57%））、以前已验证或使用NCR数据开发（n=45（5%））或关联的NCR样本大小不足而排除了模型（n=7（1%））。所包括的模型预测了生存（33个（38％）总体，27个（31％）乳腺癌特异性和3个（3％）其他特异性原因）、局部区域复发（n=7（8％））、无疾病生存期（n=7（8％））、转移（n=5（6％））、淋巴结受累（n=3（3％））、病理完全缓解（n=1（1％））和手术切缘（n=1（1％））。七个模型（8％）显示出较差的判别力（AUC＜0.6），39个（45％）中等（AUC：0.6-0.7），38个（46％）良好（AUC：0.7-0.9）和3个（3％）优秀（AUC≥0.9）。使用标度Brier分数，发现在34个（39％）模型中，性能比无信息模型差。综合登记数据支持了已发表预测模型的广泛验证。模型性能在新的患者人群中有很大差异，这证实了在将模型应用于临床实践之前进行外部验证研究的重要性。在仔细评估影响后，表现良好的模型可能在荷兰环境中具有临床应用价值。版权所有 ©2023作者。由Elsevier Ltd.出版。保留所有权利。

Numerous prediction models have been developed to support treatment-related decisions for breast cancer patients. External validation, a prerequisite for implementation in clinical practice, has been performed for only a few models. This study aims to externally validate published clinical prediction models using population-based Dutch data.Patient-, tumor- and treatment-related data were derived from the Netherlands Cancer Registry (NCR). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), scaled Brier score, and model calibration. Net benefit across applicable risk thresholds was evaluated with decision curve analysis.After assessing 922 models, 87 (9%) were included for validation. Models were excluded due to an incomplete model description (n = 262 (28%)), lack of required data (n = 521 (57%)), previously validated or developed with NCR data (n = 45 (5%)), or the associated NCR sample size was insufficient (n = 7 (1%)). The included models predicted survival (33 (38%) overall, 27 (31%) breast cancer-specific, and 3 (3%) other cause-specific), locoregional recurrence (n = 7 (8%)), disease free survival (n = 7 (8%)), metastases (n = 5 (6%)), lymph node involvement (n = 3 (3%)), pathologic complete response (n = 1 (1%)), and surgical margins (n = 1 (1%)). Seven models (8%) showed poor (AUC<0.6), 39 (45%) moderate (AUC:0.6-0.7), 38 (46%) good (AUC:0.7-0.9), and 3 (3%) excellent (AUC≥0.9) discrimination. Using the scaled Brier score, worse performance than an uninformative model was found in 34 (39%) models.Comprehensive registry data supports broad validation of published prediction models. Model performance varies considerably in new patient populations, affirming the importance of external validation studies before applying models in clinical practice. Well performing models could be clinically useful in a Dutch setting after careful impact evaluation.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.