Objective To establish a malignant risk prediction model of breast clustered ring non-mass enhancement le-sions,and to evaluate the predictive effect of the model.Methods A retrospective analysis was performed in 107 benign and malignant breast clustered ring non-enhancement lesions confirmed by operation or biopsy pathology by MRI.Multivari-ate Logistic regression analysis was used to screen the risk factors of malignant lesions.R software was used to construct a nomogram model for predicting malignant lesions.The receiver operating characteristic curve,calibration curve and deci-sion curve analysis were used to evaluate the differentiation,calibration and clinical practicability of the model.Results Single factor analysis showed that there were significant differences in maximum diameter,ADC value,early-enhancement-rate,distribution characteristics and TIC types(P<0.05).Multivariate Logistic regression analysis showed that ADC val-ue,maximum diameter and TIC type were independent risk factors for clustered ring non-mass enhancement malignant le-sions(P<0.05).The area under the receiver operating characteristic curve of the predicting and evaluating discriminative degree in nomogram model was 0.911,and the corresponding sensitivity and specificity were 89.10% and 85.20% respec-tively.The predicted value of the calibration curve was basically consistent with the actual value,and the Hosmer-Leme-show goodness-of-fit test showed =13.27,P>0.05.Compared with the independent risk factors,the decision curve of the model was farthest from the two extremes,showing a higher net benefit in the threshold range of 0-80% .Conclusion ADC value,maximum diameter and TIC type are independent risk factors for breast clustered ring non-mass enhancement malignant lesions.The malignant risk prediction nomogram model has good differentiation,calibration and clinical practica-bility,and can provide important reference for the clinical formulation of individual diagnosis and treatment plan.
BreastMagnetic resonance imagingClustered ring enhancementNomogram
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