结合术前炎症指标与临床特征的结直肠黏液腺癌诊断模型的构建与验证

Construction and validation of a diagnostic model for colorectal mucinous adenocarcinoma integrating preoperative inflammatory and clinical features

摘要:

背景与目的 结直肠黏液腺癌（MAC）是一种具有独特生物学特征的结直肠癌亚型，因其恶性程度高、术前活检确诊率低而给临床决策带来挑战。炎症微环境在MAC发生发展中作用显著。为提高术前诊断准确性，本研究旨在基于术前系统性炎症指标及临床特征，构建并验证结直肠MAC的列线图预测模型。方法 回顾性分析2017年6月—2022年6月南华大学附属第一医院行根治性切除的结直肠癌患者293例临床资料。依据术后病理结果分为非特异性腺癌（AC）组和MAC组，并采用倾向评分匹配（PSM）进行1∶1配对。比较两组术前炎症指标差异后，利用单因素及多因素Logistic回归分析筛选MAC的独立预测因子，并据此建立列线图模型。采用受试者工作特征曲线、校准曲线和决策曲线分析（DCA）评估模型的鉴别力、校准度及临床实用性。结果 MAC组46例，AC组247例，MAC术前肠镜确诊率为54%。PSM后（各组43例），MAC组术前血小板计数、血小板淋巴细胞比（PLR）、全身免疫指数（SII）、炎症相关预后指数评分（IPI）、全身炎症评分（SIS）明显高于AC组，而淋巴细胞单核细胞比（LMR）明显较低（均P<0.05）。多因素分析确定肿瘤部位、肿瘤最大径及IPI为独立预测因素。构建的列线图模型在训练队列（n=206）和验证队列（n=87）中的曲线下面积分别为0.759（95% CI=0.662~0.856）和0.776（95% CI=0.649~0.903），校准曲线拟合良好，DCA显示具有较高的临床应用价值。结论 本研究建立的基于肿瘤部位、肿瘤最大径及术前IPI的列线图模型，可有效辅助结直肠MAC的术前识别。该模型具有良好的区分度和实用性，为制定个体化治疗方案，尤其是无法手术患者的精准决策，提供了有价值的量化工具。

Abstract:

Background and Aims Mucinous adenocarcinoma of the colorectum (MAC) is a distinct histologic subtype of colorectal cancer characterized by high malignancy and low diagnostic accuracy of preoperative biopsy, posing challenges for clinical decision-making. Given the critical role of the inflammatory microenvironment in tumor progression, this study aimed to develop and validate a nomogram model integrating preoperative systemic inflammatory indicators and clinical features to improve the preoperative diagnosis of MAC.Methods Clinical data of 293 patients with colorectal cancer who underwent radical resection between June 2017 and June 2022 at the First Affiliated Hospital of the University of South China were retrospectively analyzed. Based on postoperative pathology, patients were classified into the mucinous adenocarcinoma (MAC) group and the non-specific adenocarcinoma (AC) group. Propensity score matching (PSM, 1∶1) was used to balance age, T stage, and N stage. Differences in preoperative inflammatory indices were compared between groups. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of MAC, which were incorporated into a diagnostic nomogram. The model's discrimination, calibration, and clinical utility were evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).Results Among the 293 patients, 46 had MAC and 247 had AC, with a preoperative colonoscopic diagnostic rate of 54% for MAC. After PSM (43 pairs), platelet count, platelet lymphocyte ratio (PLR), systemic immune inflammation index (SII), inflammation related prognostic index (IPI), and systemic inflammation score (SIS) were significantly higher in the MAC group, while lymphocyte monocyte ratio (LMR) was lower (all P<0.05). Multivariate analysis identified tumor location, maximum tumor diameter, and preoperative IPI as independent predictors. The AUCs of the nomogram in the training (n=206) and validation (n=87) cohorts were 0.759 (95% CI=0.662-0.856) and 0.776 (95% CI=0.649-0.903), respectively. Calibration plots showed good agreement between predicted and observed probabilities, and DCA demonstrated satisfactory clinical applicability.Conclusion A nomogram model integrating tumor location, tumor size, and preoperative IPI was successfully developed and validated for preoperative diagnosis of colorectal MAC. This model provides a practical, quantitative tool with good predictive performance to assist clinicians in individualized treatment planning, particularly for patients ineligible for surgical biopsy.