A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer

Abstract

Background: Accurate lymph node metastasis (LNM) prediction in colorectal cancer (CRC) patients is of great significance for treatment decision making and prognostic evaluation. We aimed to develop and validate a clinicalradiomics nomogram for the individual preoperative prediction of LNM in CRC patients. Methods:We enrolled 766 patients (458 in the training set and 308 in the validation set) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors (age, sex, preoperative carbohydrate antigen 19-9 (CA19-9) level, preoperative carcinoembryonic antigen (CEA) level, tumor size, tumor location, histotype, differentiation and M stage) to build the clinical model. We used analysis of variance (ANOVA), relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors and the imaging features of primary lesions and peripheral lymph nodes), established classification models with logistic regression analysis and selected the respective candidate models by fivefold cross-validation. Then, we combined the clinical risk factors, primary lesion radiomics features and peripheral lymph node radiomics features of the candidate models to establish combined predictive models. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, decision curve analysis (DCA) and a nomogram were used to evaluate the clinical usefulness of the model. Results:The clinical-primary lesion radiomics-peripheral lymph node radiomics model, with the highest AUC value (0.7606), was regarded as the candidate model and had good discrimination and calibration in both the training and validation sets. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in the clinical environment.

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“…The first article used a neural network to develop prediction models in head and neck cancer [ 23 ]. The second article developed a radiomics model based on CT images of colorectal cancer patients [ 24 ]. Both studies showed that there is potential by delineating lymph nodes for radiomics and deep learning analysis for the classification of positive and negative lymph nodes.…”

Nomograms for predicting specific distant metastatic sites and overall survival of colorectal cancer patients: A large population‐based real‐world study

“…For instance, a combination of clinical risk factors and radiomics features emphasized potential advantages to the individualized preoperative prediction of lymph node metastasis in CRC patients, which was proposed to benefit patient OS. Sun et al argued that the fibrinogen and neutrophil‐to‐lymphocyte ratio (F‐NLR score) is a promising predictor for disease relapse in rectal cancer patients 25,26 . The dissertations mentioned above were dedicated to predicting the preoperative or postoperative conditions of patients, and both might improve the prognosis of patients.…”

Development and validation of prognostic nomograms for early-onset locally advanced colon cancer

“…The general consensus exists that the postoperative specimens of radical operations for colon cancer should contain at least 12 regional lymph nodes, as recommended by the National Comprehensive Cancer Network (NCCN) guidelines [ 2 ]. However, previous research indicated that young patients with colorectal cancer suffered a higher risk of lymph node metastasis [ 23 ]. Is a minimum of 12 RNE adequate for EOLACC?…”