Zhang Wei-Hao, Huang Meng-Di, Tu Yan-Ling, Huang Kun-Zhai, Wang Chao-Jun, Liu Zhao-Hui, Ke Rui-Sheng
Department of General Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, No. 55, Zhenhai Road, Siming District, Xiamen, 361003, Fujian, China.
Xinglin Street Community Health Service Center, Jimei District, Xiamen, 361003, Fujian, China.
Clin Transl Oncol. 2025 Apr 12. doi: 10.1007/s12094-025-03903-3.
Developing a clinical model to predict the individual risk of lymph node metastasis (LNM) in young colon cancer (CC) patients may address an unmet clinical need.
A total of 2,360 CC patients under 40 years old were extracted from the SEER database and randomly divided into development and validation cohorts. Risk factors for LNM were identified by using a logistic regression model. A weighted scoring system was built according to beta coefficients (β) calculated by a logistic regression model. Model discrimination was evaluated by C-statistics, model calibration was evaluated by H-L test and calibration plot.
Risk factors were identified as T stage, tumor site, grade and histology. The area under the receiver operating characteristic curve (AUC-ROC) was 0.66 in both cohorts, indicating acceptable discriminatory power. The H-L test showed good calibration in the development cohort (χ=2.869, P=0.837) and validation cohort (χ=10.103, P=0.120) which also had been proved by calibration plot. Patients with total risk score of 0-1, 2-3 and 4-6 were considered as low, medium and high risk group.
This clinical risk prediction model is accurate enough to identify young CC patients with high risk of LNM and can further provide individualized clinical reference.
建立一种临床模型以预测年轻结肠癌(CC)患者发生淋巴结转移(LNM)的个体风险,这可能满足一项未被满足的临床需求。
从监测、流行病学和最终结果(SEER)数据库中提取了总共2360例40岁以下的CC患者,并随机分为开发队列和验证队列。使用逻辑回归模型确定LNM的危险因素。根据逻辑回归模型计算的β系数构建加权评分系统。通过C统计量评估模型辨别力,通过H-L检验和校准图评估模型校准。
危险因素被确定为T分期、肿瘤部位、分级和组织学类型。两个队列中受试者工作特征曲线下面积(AUC-ROC)均为0.66,表明具有可接受的辨别力。H-L检验显示开发队列(χ=2.869,P=0.837)和验证队列(χ=10.103,P=0.120)具有良好的校准,校准图也证实了这一点。总风险评分为0-1、2-3和4-6的患者分别被视为低、中、高风险组。
这种临床风险预测模型足够准确,能够识别具有高LNM风险的年轻CC患者,并可进一步提供个体化的临床参考。