Development and validation of a clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer

李梦蕾; 张　敬; 淡一波; 杨　光; 姚叶锋; 童　彤

doi:10.19401/j.cnki.1007-3639.2020.01.006

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Development and validation of a clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer

更新时间：2026-01-27

- Development and validation of a clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer
- China Oncology Vol. 30, Issue 1, Pages: 49-56(2020)
- 作者机构：
  
  1. 复旦大学附属肿瘤医院放射诊断科，复旦大学上海医学院肿瘤学系,上海,200032
  2. 华东师范大学上海市磁共振重点实验室,上海,200062
- 作者简介：
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2020.01.006
  CLC： R735.37
- Published Online：17 January 2020，
  
  Published：17 January 2020
- 稿件说明：
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李梦蕾, 张　敬, 淡一波, 杨　光, 姚叶锋, 童　彤 . 术前预测结直肠癌淋巴结转移的临床-影像组学列线图的建立和验证[J]. 中国癌症杂志, 2020, 30(1): 49-56.

李梦蕾, 张　敬, 淡一波, et al. Development and validation of a clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. China Oncology, 2020, 30(1): 49-56.
李梦蕾, 张　敬, 淡一波, 杨　光, 姚叶锋, 童　彤 . 术前预测结直肠癌淋巴结转移的临床-影像组学列线图的建立和验证[J]. 中国癌症杂志, 2020, 30(1): 49-56. DOI： 10.19401/j.cnki.1007-3639.2020.01.006.

李梦蕾, 张　敬, 淡一波, et al. Development and validation of a clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. China Oncology, 2020, 30(1): 49-56. DOI： 10.19401/j.cnki.1007-3639.2020.01.006.

摘要

背景与目的：术前准确预测淋巴结转移对于结直肠癌患者的肿瘤分期、治疗决策、预后及复发等至关重要。建立和验证用于术前预测结直肠癌淋巴结转移的临床-影像组学组合模型。方法：收集复旦大学附属肿瘤医院收治的767例经病理学检查确诊为结直肠癌的患者（实验组537例，验证组230例）。然后纳入9个重要临床危险因素［年龄、性别、术前癌胚抗原（carcinoembryonic antigen，CEA）水平、术前糖类抗原19-9（carbohydrate antigen 19-9，CA19-9）水平、病理学分级、组织学类型、肿瘤位置、肿瘤大小和M分期］来构建临床模型；采用ANOVA、Relief和递归特征消除（recursive feature elimination，RFE）进行特征选择（包括临床危险因素、原发病灶和周围淋巴结的影像组学特征），通过逻辑回归分析建立各自的分类模型，并通过one-standard-error准则选择最优模型，然后组合最优模型下的临床危险因素、原发灶影像组学特征、周围淋巴结影像组学特征建立联合预测模型。接着使用受试者工作特征（receiver operating characteristic，ROC）曲线及曲线下面积（area under curve，AUC）来量化预测准确率。最后应用决策曲线分析（decision curve analysis，DCA）和列线图来评估该模型的临床应用价值。结果：临床-原发灶-周围淋巴结影像组学联合模型的AUC最高（0.743 0），为最佳模型。该临床-影像组学模型在实验队列和验证队列中都显示出良好的鉴别和校正能力。DCA表明，临床-影像组学列线图在临床上具有应用价值。结论：提出了一种基于影像组学特征和临床危险因素的临床-影像组学列线图，可用于结直肠癌患者术前预测淋巴结转移。

Abstract

Background and purpose: Accurate preoperative prediction of lymph node metastasis (LNM) is very important for the prognosis and recurrence of patients with colorectal cancer (CRC). The purpose of our study was to develop and validate a clinical-radiomics nomogram for preoperative prediction of LNM for patients with CRC. Methods: We enrolled 767 patients treated in Fudan University Shanghai Cancer Center (537 in the primary cohort and 230 in the validation cohort) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors ［age

gender

preoperative carcinoembryonic antigen (CEA) level

preoperative carbohydrate antigen 19-9 (CA19-9) level

grade

histological type

tumor location

tumor size and M stage］ to build the clinical model. We used ANOVA

Relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors

imaging features of primary lesions and peripheral lymph nodes)

established the classification models through logistic regression analysis and selected respective optimal models by one-standard-error rule. Then we combined the clinical risk factors

the primary lesion radiomics features and the peripheral lymph node radiomics features of the optimal models to establish combined prediction models. The performance of the model was assessed by area under curve (AUC) of the receiver operating characteristic (ROC). Finally

decision curve analysis (DCA) and nomogram were applied to assess the clinical usefulness. Results: The clinical-primary lesion radiomics-peripheral lymph node radiomics model with the highest AUC (0.743 0) was identified as the best model. This optimal clinical-radiomics model also showed good discrimination and calibration in both primary cohort and validation cohort. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in clinical practice. Conclusion: The present study proposed a clinical-radiomics nomogram created by the radiomics signature and clinical risk factors

which can be potentially applied in the individual preoperative prediction of LNM in patients with CRC.

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Department of Gastroenterology, the Fourth Hospital of Hebei Medical University

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