建立列线图来预测原发性肺癌在初次诊断后 12、18 和 24 个月的脑转移 (BM)。在这项研究中，我们纳入了 2019 年 1 月至 2019 年 1 月期间在哈尔滨医科大学肿瘤医院诊断为原发性肺癌的 428 名患者。 2020 年和 2022 年 1 月。终点事件是 BM。患者按 7:3 的比例随机分为两组：训练组 (n = 299) 和验证组 (n = 129)。利用最小绝对收缩和选择算子来分析训练集中的实验室测试结果。此外，临床实验室组学评分是使用回归系数确定的。然后，将 clinlabomics 评分与临床数据相结合，使用随机生存森林 (RSF) 和 Cox 多元回归构建列线图。然后，采用各种方法评估列线图的性能。使用五个独立的预测因素（病理类型、直径、淋巴结转移、非淋巴结转移和临床实验室组学评分）来构建列线图。在验证集中，bootstrap C 指数为 0.7672 (95% CI 0.7092-0.8037)，12 个月 AUC 为 0.787 (95% CI 0.708-0.865)，18 个月 AUC 为 0.809 (95% CI 0.735-0.884) ，24 个月 AUC 为 0.858 (95% CI 0.792-0.924)。此外，校准曲线、决策曲线分析和Kaplan-Meier曲线显示列线图具有良好的性能。最后，我们构建并验证了列线图来预测原发性肺癌的BM风险。我们的列线图可以识别BM高风险患者，并为不同疾病时间点的临床决策提供参考。© 2024。作者。

To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis.In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram.Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram.Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.© 2024. The Author(s).