背景:大多数诊断为乳腺癌的患者存在淋巴结阴性疾病。前哨淋巴结活检(SLNB)通常用于腋窝分期,使腋窝淋巴结健康的患者没有治疗效果,但有因干预而患病的风险。许多研究已经开发了用于非侵入性腋窝分期的淋巴结状态预测模型,其使用的术后数据或成像特征不是诊断检查的一部分。淋巴管浸润(LVI)是淋巴结转移的最高预测因子;然而,其术前评估具有挑战性.
目的:本文旨在外部验证多层感知器(MLP)模型,用于基于大型人群的队列(n=18,633)中的非侵入性淋巴结分期(NILS),并在同一队列中开发新的MLP。数据来自瑞典国家乳腺癌质量登记册(NKBC,2014-2017),仅包括常规和术前可用的已记录的临床病理变量。次要目的是开发和验证LVIMLP,以填补缺失的LVI状态,以增加原始NILS模型的术前可行性。
方法:使用三个非重叠队列进行模型开发和验证。使用11至12个常规可用的预测因子开发了总共4个用于淋巴结状态的MLP和1个LVIMLP。使用三个节点状态模型来解释队列中LVI状态的不同可用性以及NKBC中的外部验证。第四节状态模型是针对80%(14,906/18,663)的NKBC病例开发的,并在其余20%(3727/18,663)中进行了验证。比较了三种估算LVI状态的替代方法。在3个节点状态模型中,使用接收器工作特征曲线(AUC)下的验证面积评估了判别能力。使用校准和决策曲线分析评估模型的临床可行性。
结果:在NKBC(AUC0.699,95%CI0.690-0.708)中对原始NILS模型进行了外部验证,校准良好,并且有可能使16%的淋巴结阴性患者免于SLNB。LVI模型经过外部验证(AUC0.747,95%CI0.694-0.799),校准良好,但未改善节点状态模型的判别性能。在没有LVI信息的NKBC中开发了新的节点状态模型(AUC0.709,95%CI:0.688-0.729),在保持内部验证队列中具有出色的校准,导致24%的患者可能从不必要的SLNB中遗漏。
结论:NILS模型在NKBC中进行了外部验证,其中LVI状态的归因并没有改善模型的歧视性表现。新的节点状态模型证明了使用寄存器数据的可行性,该寄存器数据仅包括使用机器学习进行NILS的术前设置中可用的变量。未来的步骤包括正在进行的NILS模型的术前验证和扩展模型,例如,乳房X线摄影图像。
BACKGROUND: Most patients diagnosed with breast cancer present with a node-negative disease. Sentinel lymph node biopsy (
SLNB) is routinely used for axillary staging, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of nodal metastasis; however, its preoperative assessment is challenging.
OBJECTIVE: This paper aimed to externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based cohort (n=18,633) and develop a new MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC, 2014-2017), comprising only routinely and preoperatively available documented clinicopathological variables. A secondary aim was to develop and validate an LVI MLP for imputation of missing LVI status to increase the preoperative feasibility of the original NILS model.
METHODS: Three nonoverlapping cohorts were used for model development and validation. A total of 4 MLPs for nodal status and 1 LVI MLP were developed using 11 to 12 routinely available predictors. Three nodal status models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth nodal status model was developed for 80% (14,906/18,663) of NKBC cases and validated in the remaining 20% (3727/18,663). Three alternatives for imputation of LVI status were compared. The discriminatory capacity was evaluated using the validation area under the receiver operating characteristics curve (AUC) in 3 of the nodal status models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses.
RESULTS: External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI 0.690-0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from
SLNB. The LVI model was externally validated (AUC 0.747, 95% CI 0.694-0.799) with good calibration but did not improve the discriminatory performance of the nodal status models. A new nodal status model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688-0.729), with excellent calibration in the holdout internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNBs.
CONCLUSIONS: The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model\'s discriminatory performance. A new nodal status model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images.