Premium
Prediction Model Combining Clinical and MR Data for Diagnosis of Lymph Node Metastasis in Patients With Rectal Cancer
Author(s) -
Xu Hanshan,
Zhao Wenyuan,
Guo Wenbing,
Cao Shaodong,
Gao Chao,
Song Tiantian,
Yang Liping,
Liu Yanlong,
Han Yu,
Zhang Lingbo,
Wang Kezheng
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27369
Subject(s) - medicine , logistic regression , colorectal cancer , stage (stratigraphy) , radiology , magnetic resonance imaging , lymph node , univariate analysis , metastasis , cancer , multivariate analysis , paleontology , biology
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 2 ‐weighted turbo spin echo and diffusion‐weighted imaging (b = 0 s/mm 2 , 800 s/mm 2 ) 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