非小细胞肺癌（NSCLC）在脑转移（BM）的发生率很高。早期检测对于改善临床前景至关重要。我们训练和验证了分类器模型，以识别有高患脑转移风险的患者，他们可能会从监测性脑MRI中获益。我们在德国肺癌中心回顾性地招募了2011年1月至2019年4月间进行NSCLC初诊的连续患者，并进行了开局胸部CT扫描（分期）。在初诊时和出现神经症状时进行脑成像检查（随访）。在数据截止日期（2020年12月）时，失访的受试者或尚未出现BM的仍然存活的受试者被排除。协变量包括初期分期胸部CT中原发肿瘤的临床特征和/或3D放射影像特征。比较了四个用于预测的机器学习模型（80/20训练）。作为重要性指标，使用了Gini重要性和SHAP；作为评价指标，使用了敏感性、特异性、精度-召回曲线下面积和马修斯相关系数。共有395名患者构成了临床队列。基于临床特征的预测模型表现最佳（调整以最大化回收率：敏感性约70%，特异性约60%）。放射影像特征未能提供足够的信息，可能由于成像数据的异质性。腺癌组织学分型、淋巴结浸润和组织学肿瘤分级与BM预测呈正相关，年龄和鳞状细胞癌组织学分型呈负相关。亚组发现分析发现了两个患者亚群，似乎存在较高的BM风险（女性患者+腺癌组织学分型，腺癌患者+无其他远处转移）。输入特征重要性的分析表明，模型正在学习临床特征与BM发生之间的相关关系。需要优先考虑增加样本数量以提高性能。这样的模型在初诊时进行前瞻性应用，可以帮助选择高风险亚组进行监测性脑MRI。版权 © 2023 作者。由Elsevier Inc.出版。保留所有权利。

Non-small-cell lung cancer (NSCLC) shows a high incidence of brain metastases (BM). Early detection is crucial to improve clinical prospects. We trained and validated classifier models to identify patients with a high risk of developing BM, as they could potentially benefit from surveillance brain MRI.Consecutive patients with an initial diagnosis of NSCLC from January 2011 to April 2019 and an in-house chest-CT scan (staging) were retrospectively recruited at a German lung cancer center. Brain imaging was performed at initial diagnosis and in case of neurological symptoms (follow-up). Subjects lost to follow-up or still alive without BM at the data cut-off point (12/2020) were excluded. Covariates included clinical and/or 3D-radiomics-features of the primary tumor from staging chest-CT. Four machine learning models for prediction (80/20 training) were compared. Gini Importance and SHAP were used as measures of importance; sensitivity, specificity, area under the precision-recall curve, and Matthew's Correlation Coefficient as evaluation metrics.Three hundred and ninety-five patients compromised the clinical cohort. Predictive models based on clinical features offered the best performance (tuned to maximize recall: sensitivity∼70%, specificity∼60%). Radiomics features failed to provide sufficient information, likely due to the heterogeneity of imaging data. Adenocarcinoma histology, lymph node invasion, and histological tumor grade were positively correlated with the prediction of BM, age, and squamous cell carcinoma histology were negatively correlated. A subgroup discovery analysis identified 2 candidate patient subpopulations appearing to present a higher risk of BM (female patients + adenocarcinoma histology, adenocarcinoma patients + no other distant metastases).Analysis of the importance of input features suggests that the models are learning the relevant relationships between clinical features/development of BM. A higher number of samples is to be prioritized to improve performance. Employed prospectively at initial diagnosis, such models can help select high-risk subgroups for surveillance brain MRI.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.