乳腺癌切除术后放疗前哨淋巴结状态预测模型的开发与验证:一项针对 18 185 名妇女的人群研究

Miriam Svensson, Par-Ola Bendahl, Sara Alkner, Emma Hansson, Lisa Ryden, Looket Dihge
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引用次数: 0

摘要

背景:乳房切除术后放疗(PMRT)会影响乳腺癌患者即刻乳房重建(IBR)的效果,而前哨淋巴结(SLN)状态是评估是否需要进行PMRT的关键。本研究旨在建立模型,以便在术前预测SLN转移的风险,从而确定是否需要进行PMRT:2014年1月至2017年12月期间诊断为临床结节阴性(cN0)T1-T2乳腺癌的女性均在瑞典国家乳腺癌质量登记册中进行了确认。根据术前可获得的患者和肿瘤特征,采用自适应LASSO逻辑回归法绘制了结节预测提名图。≥1个和>2个SLN大转移(macro-SLNMs)的预测符合目前的PMRT使用指南,并反映了正在进行的临床试验的排除标准,这些临床试验旨在分别降低1-2个macro-SLNMs患者的局部放疗等级。使用接收者操作特征曲线下面积(AUC)和校准图评估了预测性能。结果:共有 18 185 名妇女被分为训练组(n = 13 656)和验证组(n = 4529)。校准良好的预测≥1和>2宏SLNM的提名图在验证时的AUC分别为0.708和0.740。通过使用≥1宏SLNM的提名图,可将风险从检测前的13%更新到检测后的2%-75%。结论基于常规患者和肿瘤特征的提名图可用于预测SLN状态,以显示是否需要进行PMRT,并帮助cN0乳腺癌患者做出IBR决策。
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Development and Validation of Prediction Models for Sentinel Lymph Node Status Indicating Postmastectomy Radiotherapy in Breast Cancer: a Population-Based Study of 18 185 Women
Background: Postmastectomy radiotherapy (PMRT) impairs the outcome of immediate breast reconstruction (IBR) in patients with breast cancer, and the sentinel lymph node (SLN) status is crucial in evaluating the need for PMRT. This study aimed to develop models to preoperatively predict the risk for SLN metastasis indicating the need for PMRT. Methods: Women diagnosed with clinically node-negative (cN0) T1-T2 breast cancer from January 2014 to December 2017 were identified within the Swedish National Quality Register for Breast Cancer. Nomograms for nodal prediction based on preoperatively accessible patient and tumor characteristics were developed using adaptive LASSO logistic regression. The prediction of ≥1 and >2 SLN macrometastases (macro-SLNMs) adheres to the current guidelines on use of PMRT and reflects the exclusion criteria in ongoing clinical trials aiming to de-escalate locoregional radiotherapy in patients with 1-2 macro-SLNMs, respectively. Predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and calibration plots. Results: Overall, 18 185 women were grouped into training (n =13 656) and validation (n = 4529) cohorts. The well-calibrated nomograms predicting ≥1 and >2 macro-SLNMs displayed AUCs of 0.708 and 0.740, respectively, upon validation. By using the nomogram for ≥1 macro-SLNMs, the risk could be updated from the pre-test population prevalence 13% to the post-test range 2%-75%. Conclusion: Nomograms based on routine patient and tumor characteristics could be used for prediction of SLN status that would indicate PMRT need and assist the decision-making on IBR for patients with cN0 breast cancer.
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