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中华普通外科学文献(电子版) ›› 2025, Vol. 19 ›› Issue (06) : 383 -389. doi: 10.3877/cma.j.issn.1674-0793.2025.06.005

论著

基于一种新炎症-营养指标构建结直肠癌术前淋巴结转移预测模型
徐世伟1,,2, 廖杜荣1,,2, 张镐1,,2, 叶辉1, 陈志平3, 雒洪志1,,2,()   
  1. 1 524023 湛江,广东医科大学
    2 528403 中山,中山市人民医院肿瘤外科
    3 518060 深圳,深圳大学
  • 收稿日期:2025-02-25 出版日期:2025-12-01
  • 通信作者: 雒洪志
  • 基金资助:
    2023年度中山市第三批社会公益与基础研究项目(2023B3018)

Construction of a predictive model for preoperative lymph node metastasis in colorectal cancer based on a new inflammatory-nutritional indicator, PALR

Shiwei Xu1,,2, Durong Liao1,,2, Hao Zhang1,,2, Hui Ye1, Zhiping Chen3, Hongzhi Luo1,,2,()   

  1. 1 Guangdong Medical University, Zhanjiang 524023, China
    2 Department of Surgical Oncology, Zhongshan City People’s Hospital, Zhongshan 528403, China
    3 Shenzhen University, Shenzhen 518060, China
  • Received:2025-02-25 Published:2025-12-01
  • Corresponding author: Hongzhi Luo
引用本文:

徐世伟, 廖杜荣, 张镐, 叶辉, 陈志平, 雒洪志. 基于一种新炎症-营养指标构建结直肠癌术前淋巴结转移预测模型[J/OL]. 中华普通外科学文献(电子版), 2025, 19(06): 383-389.

Shiwei Xu, Durong Liao, Hao Zhang, Hui Ye, Zhiping Chen, Hongzhi Luo. Construction of a predictive model for preoperative lymph node metastasis in colorectal cancer based on a new inflammatory-nutritional indicator, PALR[J/OL]. Chinese Archives of General Surgery(Electronic Edition), 2025, 19(06): 383-389.

目的

探究一种新的炎症-营养指标联合血清标志物—(血小板×白蛋白)/淋巴细胞比值(PALR)对结直肠癌淋巴结转移的影响,并建立临床适用的列线图风险预测模型。

方法

收集2022年7月至2023年7月中山市人民医院收治的235例行结直肠癌根治术患者临床资料,按7∶3比例随机将研究对象分为训练集(164例)和验证集(71例)。采用单因素分析初步筛选结直肠癌淋巴结转移的相关因素,再基于多因素Logistic回归分析确定的独立危险因素开发列线图预测模型,并采用验证集对模型进行内部验证;同时,利用受试者工作特征(ROC)曲线、校准图以及决策曲线分析(DCA)综合评估该模型的预测效能及其在临床实践中的应用价值。

结果

训练集患者依据淋巴结转移状态,分为转移阳性组(116例)和转移阴性组(48例)。回归分析显示,PALR、系统免疫炎症指数(SII)、癌胚抗原(CEA)水平、肿瘤分化程度、术前肠梗阻以及增强CT报告淋巴结转移均与结直肠癌患者发生淋巴结转移显著相关(P<0.05)。依此建立列线图预测模型,训练集和验证集的ROC曲线下面积分别为0.868(95% CI:0.813~0.922)和0.854(95% CI:0.763~0.946)。验证集模型验证结果显示,校准曲线与实际观测值具有良好的一致性。DCA证实该预测模型在临床应用中具有较高的实用价值。

结论

基于PALR构建的列线图预测模型为术前评估结直肠癌患者淋巴结转移风险提供了一个关键性工具,为临床制定个性化精准治疗方案提供了有益的参考和支持。

Objective

To investigate the effect of a new combined inflammatory-nutritional serum marker, (platelet×albumin) to lymphocyte ratio (PALR), on lymph node metastasis in colorectal cancer (CRC), and to establish a clinically applicable risk prediction model for column line drawing.

Methods

The clinical data of 235 CRC radical surgery patients in Zhongshan City People’s Hospital from July 2022 to July 2023 were collected, and the study subjects were randomly divided into the training set and the validation set according to the ratio of 7∶3. A single-factor analysis was used to initially screen the factors associated with CRC lymph node metastasis, and then a column-line graph prediction model was developed based on the independent risk factors identified by multifactor logistic regression analysis, and a validation set was used to internally validate the model, and receiver operating characteristic (ROC) curves, calibration plots, and decision analytic curves.

Results

Patients in the training set were divided into metastasis positive group (116 cases) and metastasis negative group (48 cases) based on their lymph node metastasis status. The results of regression analysis showed that PALR, systemic immune-inflammation index (SII), carcinoembryonic antigen (CEA) level, degree of tumor differentiation, preoperative intestinal obstruction, and enhanced CT report of lymph node metastasis were all significantly correlated with the occurrence of lymph node metastasis in CRC patients (P<0.05). The areas under the curve of the training and validation sets of the model were 0.868 (95% CI: 0.813-0.922) and 0.854 (95% CI: 0.763-0.946), respectively, and the validation results of the model in the validation set showed that the calibration curves were in good agreement with the actual observed values, and meanwhile, the assessment based on the decision curves confirmed that the predictive tool had a higher practical value.

Conclusion

The nomogram prediction model based on PALR provides a key tool for CRC patients to assess the risk of lymph node metastasis before surgery, which is helpful to provide useful reference and support for personalized and precise treatment plans for patients.

表1 训练集和验证集临床资料比较
项目 训练集(164例) 验证集(71例) 统计值 P
性别a 0.004 0.947
87(53.0) 38(53.5)
77(47.0) 33(46.5)
年龄(岁)b 62.8±12.1 65.5±11.2 1.616 0.540
BMI (kg/m2)c 23.40(20.9,25.3) 23.9(21.5,25.8) 5 167.000 0.171
吸烟史a - 1.000
9 (5.5) 3 (4.2)
155 (94.5) 68 (95.8)
饮酒史a - 0.757
8 (4.9) 4 (5.6)
156 (95.1) 67 (94.4)
糖尿病a 0.001 0.978
21 (12.8) 9 (12.7)
143 (87.2) 62 (87.3)
高血压a 0.053 0.817
58 (35.4) 24 (33.8)
106 (64.6) 47 (66.2)
冠心病a - 1.000
11 (6.7) 5 (7.0)
153 (93.3) 66 (93.0)
贫血a 0.029 0.864
55 (33.5) 23 (32.4)
109 (66.5) 48 (67.6)
低蛋白血症a 0.574 0.412
17 (10.4) 10 (14.1)
147 (89.6) 61 (85.9)
术前肠梗阻a 0.410 0.522
21 (12.8) 7 (9.9)
143 (87.2) 64 (90.1)
肿瘤位置a 3.086 0.214
直肠 53 (32.3) 29 (40.8)
左结肠 68 (41.5) 21 (29.6)
右结肠 43 (26.2) 21 (29.6)
肿瘤分化程度a - 0.573
10 (6.1) 6 (8.6)
高-中 154 (93.9) 65 (91.4)
组织学分型a - 0.318
腺癌 160 (97.6) 71 (100.0)
其他 4 (2.4) 0 (0)
增强CT报告LNMa 0.022 0.883
阴性 101 (61.6) 43 (60.6)
阳性 63 (38.4) 28 (39.4)
CEA (U/ml)a 3.669 0.055
≤5 124 (75.6) 45 (63.4)
>5 40 (24.4) 26 (36.6)
CA19-9 (U/ml)a 2.874 0.090
≤37 132 (80.5) 50 (70.4)
>37 32 (19.5) 21 (29.6)
SII a 1.150 0.284
≤549.7 73 (44.5) 37 (52.1)
>549.7 91 (55.5) 34 (47.9)
PNIa 0.873 0.350
≤61.9 162 (98.8) 71 (100.0)
>61.9 2 (1.2) 0 (0)
PALRa 3.684 0.055
≤4.8 42 (25.6) 27 (38.0)
>4.8 122 (74.4) 44 (62.0)
表2 基于训练集的结直肠癌患者淋巴结转移风险单因素Logistic回归分析
表3 基于训练集的结直肠癌患者淋巴结转移风险多因素Logistic回归分析
图1 结直肠癌淋巴结转移列线图风险预测模型 淋巴结转移(LNM);癌胚抗原(CEA);系统免疫炎症指数(SII);(血小板×白蛋白)/淋巴细胞比值(PALR)
图2 结直肠癌淋巴结转移预测模型的受试者工作特征曲线 A为训练集;B为验证集
图3 基于训练集的各独立危险因素单独预测的受试者工作特征曲线 癌胚抗原(CEA);系统免疫炎症指数(SII);(血小板×白蛋白)/淋巴细胞比值(PALR)
图4 结直肠癌淋巴结转移预测模型的校准曲线 A为训练集;B为验证集
图5 基于训练集的结直肠癌淋巴结转移预测模型的临床决策曲线
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