Objective To construct and validate a nomogram model based on extracellular volume fraction(ECV)and clinical factors for predicting the expression of p53 in colorectal cancer(CRC).Methods 147 patients with CRC who underwent surgery in our hospital from January 2019 to March 2022 were selected retrospectively.According to pathology,they were divided into two groups:high risk p53 expression group and low risk p53 expression group.Patients were random-ly divided into training group(103 cases)and validation group(44 cases).Logistic regression(LR)and lasso regression(LASSO)was used respectively to screen the variables with predictive value,then established several predictive models.The diagnostic efficacy was compared using ROC curves.A nomogram was built basing on the model whose area under the curve was the biggest.The efficiency and clinical benefit of the nomogram model were evaluated by degree of differentia-tion,calibration and decision curve analysis(DC A).Results The results of multiple Logistic regression showed that sex(OR=2.073,P＜0.05),Maximum diameter of tumor(OR=1.023,P＜0.05)and ECV(OR=0.958,P＜0.05)were independent predictors of p53 high risk expression.LASSO regression screened out 6 potential predictive factors,which was sex,maximum diameter of tumor,ECV,△HU tumor,Carbohydrate antigen 199(CA199),neutrophil to lymphocyte ratio(NLR).ROC curves showed that the nomogram model based on LASSO regression has better prediction efficiency(AUC=0.752,95％CI:0.605-0.899,P=0.005).And the calibration curve and DC A curve indicated good calibration and clini-cal benefit of the model.Conclusion The nomogram model constructed by combining ECV and clinical indicators has good discrimination,calibration and clinical benefit,which can be used to identify CRC patients with p53 high-risk expres-sion non-invasively,and provide reference for clinical diagnosis and treatment.
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