肿瘤最大厚度在磁共振直肠癌T分期中的诊断预测价值

Diagnostic and Predictive Value of Maximum Tumor Thickness in MRI T Staging of Rectal Cancer

摘要: 目的：探讨直肠癌灶的最大厚度在磁共振评估直肠癌T分期方面的应用价值。方法：采用回顾性研究，收集来自安徽医科大学第二附属医院2022年05月~2025年06月期间的300例直肠癌患者磁共振影像学图片和临床资料，以病理T分期为分组，进行肿瘤最大厚度和T分期之间的单因素相关分析、Logistic回归分析以及预测分析。结果：研究发现肿瘤最大厚度、CEA、白蛋白水平、肿瘤长径、阳性淋巴结数目与直肠癌T分期程度具有相关性，差异有统计学意义(P < 0.05)。年龄、性别、BMI与T分期之间相关性无统计学意义(P > 0.05)。多因素分析显示肿瘤最大厚度为预测直肠癌T分期的独立危险因素(P < 0.01)。逐步回归模型(包含厚度、长径和淋巴结数目)的AUC为0.779，具有中等预测效能。结论：肿瘤最大厚度是直肠癌T分期的独立预测因素，基于肿瘤最大厚度的仅厚度模型具有良好预测效能(AUC = 0.812)。在诊断困难病例中，厚度指标辅助放射科医生可将诊断准确率由0%提升至57.4%，为临床T分期评估提供了简便、可量化的辅助工具。

Abstract: Objective: To investigate the clinical value of maximum tumor thickness in magnetic resonance imaging (MRI) assessment of rectal cancer T staging. Methods: A retrospective study was conducted, collecting MRI images and clinical data of 300 rectal cancer patients from the Second Affiliated Hospital of Anhui Medical University between May 2022 and June 2025. Patients were grouped according to pathological T stage to perform univariate correlation analysis, logistic regression analysis, and predictive analysis between maximum tumor thickness and T stage. Results: The study found that maximum tumor thickness, CEA, albumin levels, tumor length, and the number of positive lymph nodes were correlated with rectal cancer T stage, with statistically significant differences (P < 0.05). Age, gender, and BMI showed no statistically significant correlation with T stage (P > 0.05). Multivariate analysis indicated that maximum tumor thickness was an independent risk factor for predicting rectal cancer T stage (P < 0.01). The stepwise regression model (including thickness, length, and lymph node number) had an AUC of 0.779, indicating moderate predictive efficacy. Conclusion: Maximum tumor thickness is an independent predictive factor for T staging of rectal cancer. A thickness-only model based on maximum tumor thickness has good predictive performance (AUC = 0.812). In diagnostically challenging cases, the thickness metric can assist radiologists in increasing diagnostic accuracy from 0% to 57.4%, providing a simple and quantifiable tool for clinical T staging assessment.