Deng Longlian, Che Lemuge, Sun Haibin, En Riletu, Ha Bowen, Liu Tao, Wang Tengqi, Xu Qiang
Department of Abdominal Oncology, the Second People's Hospital of Neijiang, Neijiang, 641000, China.
Department of Gastrointestinal Surgery, Inner Mongolia Bayannur Hospital, Bayannur, 015000, China.
BMC Gastroenterol. 2025 May 8;25(1):350. doi: 10.1186/s12876-025-03958-0.
Predicting lymph node metastasis (LNM) in colon cancer (CC) is crucial to treatment decision-making and prognosis. This study aimed to develop and validate a nomogram that estimates the risk of LNM in patients with CC using multiple clinical data from patients before surgery.
Clinicopathological data were collected from 412 CC patients who underwent Radical resection of CC. The training cohort consisted of 300 cases, and the external validation cohort consisted of 112 cases. The LASSO and multivariate logistic regression were used to select the predictors and construct the nomogram. The discrimination and calibration of the nomogram were evaluated by the ROC curve and calibration curve, respectively. The clinical application of the nomogram was assessed by decision curve analysis(DCA) and clinical impact curves(CIC).
Eight independent factors associated with LNM were identified by multivariate logistic analysis: LN status on CT, tumor diameter on CT, differentiation, ulcer, intestinal obstruction, anemia, blood type, and neutrophil percentage. The online dynamic nomogram model constructed by independent factors has good discrimination and consistency. The AUC of 0.834(95% CI: 0.755-0.855) in the training cohort, 0.872(95%CI: 0.807-0.937) in the external validation cohort, and Internal validation showed that the corrected C statistic was 0.810. The calibration curve of both the training set and the external validation set indicated that the predicted outcome of the nomogram was highly consistent with the actual outcome. The DCA and CIC indicate that the model has clinical practical value.
Based on various simple parameters collected preoperatively, the online dynamic nomogram can accurately predict LNM risk in CC patients. The high discriminative ability and significant improvement of NRI and IDI indicate that the model has potential clinical application value.
预测结肠癌（CC）中的淋巴结转移（LNM）对于治疗决策和预后至关重要。本研究旨在开发并验证一种列线图，该列线图使用手术前患者的多个临床数据来估计CC患者发生LNM的风险。
收集了412例行CC根治性切除术患者的临床病理数据。训练队列由300例病例组成，外部验证队列由112例病例组成。使用LASSO和多因素逻辑回归来选择预测因素并构建列线图。分别通过ROC曲线和校准曲线评估列线图的辨别力和校准度。通过决策曲线分析（DCA）和临床影响曲线（CIC）评估列线图的临床应用。
多因素逻辑分析确定了8个与LNM相关的独立因素：CT上的淋巴结状态、CT上的肿瘤直径、分化程度、溃疡、肠梗阻、贫血、血型和中性粒细胞百分比。由独立因素构建的在线动态列线图模型具有良好的辨别力和一致性。训练队列中的AUC为0.834（95%CI：0.755 - 0.855），外部验证队列中的AUC为0.872（95%CI：0.807 - 0.937），内部验证显示校正后的C统计量为0.810。训练集和外部验证集的校准曲线均表明列线图的预测结果与实际结果高度一致。DCA和CIC表明该模型具有临床实用价值。
基于术前收集的各种简单参数，在线动态列线图可以准确预测CC患者的LNM风险。较高的辨别能力以及NRI和IDI的显著改善表明该模型具有潜在的临床应用价值。
