临床与磁共振数据联合预测模型在直肠癌患者淋巴结转移诊断中的应用

Prediction Model Combining Clinical and MR Data for Diagnosis of Lymph Node Metastasis in Patients With Rectal Cancer.

机构信息

PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin City, China.

Bioinformatics Science and Technology College, Harbin Medical University, Harbin City, China.

出版信息

J Magn Reson Imaging. 2021 Mar;53(3):874-883. doi: 10.1002/jmri.27369. Epub 2020 Sep 26.

DOI:10.1002/jmri.27369

PMID:32978993

Abstract

BACKGROUND

Determining the status of lymph node (LN) metastasis in rectal cancer patients preoperatively is crucial for the treatment option. However, the diagnostic accuracy of current imaging methods is low.

PURPOSE

To develop and test a model for predicting metastatic LNs of rectal cancer patients based on clinical data and MR images to improve the diagnosis of metastatic LNs.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 341 patients with histologically confirmed rectal cancer were divided into one training set (120 cases) and three validation sets (69, 103, 49 cases).

FIELD STRENGTH/SEQUENCE: 3.0T, axial and sagittal T -weighted turbo spin echo and diffusion-weighted imaging (b = 0 s/mm , 800 s/mm ) ASSESSMENT: In the training dataset, univariate logistic regression was used to identify the clinical factors (age, gender, and tumor markers) and MR data that correlated with LN metastasis. Then we developed a prediction model with these factors by multiple logistic regression analysis. The accuracy of the model was verified using three validation sets and compared with the traditional MRI method.

STATISTICAL TESTS

Univariate and multivariate logistic regression. The area under the curve (AUC) value was used to quantify the diagnostic accuracy of the model.

RESULTS

Eight factors (CEA, CA199, ADCmean, mriT stage, mriN stage, CRM, EMVI, and differentiation degree) were significantly associated with LN metastasis in rectal cancer patients (P<0.1). In the training set (120) and the three validation sets (69, 103, 49), the AUC values of the model were much higher than the diagnosis by MR alone (training set, 0.902 vs. 0.580; first validation set, 0.789 vs. 0.743; second validation set, 0.774 vs. 0.573; third validation set, 0.761 vs. 0.524).

DATA CONCLUSION

For the diagnosis of metastatic LNs in rectal cancer patients, our proposed logistic regression model, combining clinical and MR data, demonstrated higher diagnostic efficiency than MRI alone.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2.

摘要

背景

术前确定直肠癌患者淋巴结（LN）转移状态对治疗方案至关重要。然而，目前影像学方法的诊断准确性较低。

目的

基于临床数据和磁共振（MR）图像开发并测试一种预测直肠癌患者转移性 LN 的模型，以提高转移性 LN 的诊断准确性。

研究类型

回顾性。

受试者

所有经组织学证实的直肠癌患者 341 例，分为训练集（120 例）和三个验证集（69、103、49 例）。

磁场强度/序列：3.0T，轴位和矢状位 T1 加权涡轮自旋回波和弥散加权成像（b=0 s/mm²，800 s/mm²）。

评估

在训练数据集中，采用单因素逻辑回归识别与 LN 转移相关的临床因素（年龄、性别和肿瘤标志物）和 MR 数据。然后，我们通过多因素逻辑回归分析用这些因素建立预测模型。使用三个验证集验证模型的准确性，并与传统 MRI 方法进行比较。

统计学检验

单因素和多因素逻辑回归。曲线下面积（AUC）值用于量化模型的诊断准确性。

结果

8 个因素（CEA、CA199、ADCmean、mriT 分期、mriN 分期、CRM、EMVI 和分化程度）与直肠癌患者的 LN 转移显著相关（P<0.1）。在训练集（120 例）和三个验证集（69、103、49 例）中，模型的 AUC 值均明显高于单独 MRI 诊断（训练集：0.902 比 0.580；第一验证集：0.789 比 0.743；第二验证集：0.774 比 0.573；第三验证集：0.761 比 0.524）。