Nierenberg Tori C, Dalton Juliet C, Howell Rylan, Kaplan Samantha, Chiba Akiko, Wang Ton, Plichta Jennifer K
Department of Surgery, Duke University Medical Center, Durham, NC, USA.
University of Nevada, Reno School of Medicine, Reno, NV, USA.
Am J Surg. 2025 Sep 2;250:116598. doi: 10.1016/j.amjsurg.2025.116598.
Breast atypia is a benign breast disease found in a minority of percutaneous biopsies and is associated with an increased risk of breast cancer. Risk assessment calculators/tools have variable performance in this subgroup. We systematically reviewed tools developed or validated for women with breast atypia to guide clinicians and inform future model development.
We searched three databases for original studies published prior to November 13, 2023, that investigated the development or validation of risk assessment tools in atypia patients. PROBAST was used to assess the risk of bias and applicability.
From 6252 records, 12 studies (1993-2022) were included (91.6 % U.S.-based; median sample size 540). The Gail model (C-index 0.5) and Tyrer-Cuzick model (0.49-0.54) performed poorly. Newer models demonstrated improved calibration (0.59-0.68), though 41.6 % had high risk of bias.
Current tools inadequately predict breast cancer risk in atypia patients. Novel, population-specific models are needed.
乳腺非典型增生是一种在少数经皮活检中发现的良性乳腺疾病，与乳腺癌风险增加相关。风险评估计算器/工具在该亚组中的表现各异。我们系统回顾了为乳腺非典型增生女性开发或验证的工具，以指导临床医生并为未来模型开发提供信息。
我们检索了三个数据库，查找2023年11月13日之前发表的关于非典型增生患者风险评估工具开发或验证的原始研究。采用PROBAST评估偏倚风险和适用性。
从6252条记录中，纳入了12项研究（1993 - 2022年）（91.6%基于美国；样本量中位数为540）。盖尔模型（C指数0.5）和泰勒 - 库齐克模型（0.49 - 0.54）表现不佳。较新的模型校准有所改善（0.59 - 0.68），不过41.6%有高偏倚风险。
当前工具无法充分预测非典型增生患者的乳腺癌风险。需要新的、针对特定人群的模型。
