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

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 T2‐weighted turbo spin echo and diffusion‐weighted imaging (b = 0 s/mm2, 800 s/mm2) 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

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

“…Previous studies have reported the diagnostic value of radiomics models in predicting LN metastasis in rectal cancer ( 17 – 19 , 28 – 34 ). Huang et al.…”

Radiomics in colorectal cancer

“…Radiomics‐based approaches enable the assessment of tumor characteristics, such as size, shape, texture, and heterogeneity, thus aiding the prediction of treatment outcomes and selection of optimal treatment strategies [22]. Combined with clinical data and molecular markers, radiomic features contribute to the development of precision medicine for CRC [23]. Radiomics identifies imaging‐based biomarkers of treatment response and prognosis.…”

Magnetic Resonance Imaging Evaluation of the Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer: A Systematic Review and Meta-Analysis

“…The characteristics of the studies are presented in Table 1. The reference standards were divided into the following five categories according to different morphological criteria: (A) a short-axis diameter of 5 mm (22-34), (B) morphological standard, including an irregular border and mixed-signal intensity within the lymph node (35)(36)(37)(38)(39)(40), (C) a short-axis diameter of 5 mm with the morphological standard (11,(41)(42)(43)(44)(45)(46)(47)(48), (D) a short-axis diameter of 8 mm with the morphological standard (49)(50)(51)(52), and (E) a short-axis diameter of 10 mm with the morphological standard (11,45,53,54). In all of the included articles, 36 indirectly evaluated the lymph node stage of patients through histopathology and 5 (9,41,42,55,56) identified the node-by-node correspondence between lymph node MRI scans and histopathologic results.…”