Ge Xiaoqin, Li Dan, Ye Xiaoxian, Ma Ruishuang, Yuan Ying
Department of Medical Oncology, Ningbo First Hospital, Zhejiang, 315000, Ningbo, China.
Department of Medical Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Zhejiang, 310000, Hangzhou, China.
Int J Colorectal Dis. 2022 Dec;37(12):2469-2480. doi: 10.1007/s00384-022-04289-2. Epub 2022 Dec 2.
We conducted this study to explore clinicopathological profiles of brain metastases (BM) and establish a clinical prediction model that predicts the presence of BM in colorectal cancer (CRC) patients.
Patients with initially diagnosed CRC were reviewed between the year 2010 and 2015. Multiple imputations are used for handling missing values. Prognostic factors were identified by the univariate and multivariate Cox regression model. Univariate and multivariate logistic regression was used to identify the predictive factors for the presence of BM. A nomogram was constructed based on statistically significant risk factors of the presence of BM. The decision curve analysis (DCA) was used to assess the clinical usefulness and net benefits of the nomogram for the presence of BM.
Four hundred ninety-five patients with brain metastasis at the initial diagnosis were identified, representing 0.24% of the whole cohort and 0.91% of the metastatic cohort. Multivariable logistic regression demonstrated that young age, positive CEA, adenocarcinoma, lower differentiated grade, presence of liver metastases, presence of lung metastases, and presence of bone metastases were significantly associated with higher risk of developing BM. The decision curve analysis inform clinical decisions were better than a scenario in which all patients or no patients are screened across a wide range of threshold at ≥ 0.027%.
The risk estimates provided by the nomogram can be extremely useful for earlier diagnosis, especially when discussing screening strategy among high-risk patients.
我们开展这项研究以探索脑转移瘤（BM）的临床病理特征，并建立一个预测结直肠癌（CRC）患者发生BM的临床预测模型。
对2010年至2015年期间初诊为CRC的患者进行回顾。采用多重填补法处理缺失值。通过单因素和多因素Cox回归模型确定预后因素。使用单因素和多因素逻辑回归来确定BM存在的预测因素。基于BM存在的统计学显著危险因素构建列线图。采用决策曲线分析（DCA）评估该列线图对BM存在情况的临床实用性和净效益。
共识别出495例初诊时伴有脑转移的患者，占整个队列的0.24%，占转移队列的0.91%。多因素逻辑回归显示，年轻、癌胚抗原（CEA）阳性、腺癌、低分化程度、存在肝转移、存在肺转移和存在骨转移与发生BM的较高风险显著相关。决策曲线分析表明，在≥0.027%的广泛阈值范围内，临床决策比所有患者都进行筛查或都不进行筛查的情况更好。
列线图提供的风险估计对于早期诊断非常有用，尤其是在讨论高危患者的筛查策略时。
