Abstract: Objective To evaluate the value of Kaiser score⁃based MRI feature nomogram model in preoperative prediction of vascular invasion in mass breast cancer. Methods The data of clinical, pathological, imaging and Kaiser score of 345 patients with mass invasive breast cancer confirmed by surgical pathology were retrospectively analyzed. The patients were randomly divided into training set (n=242) and validation set (n=103) according to a ratio of 7∶3. Univariable and multivariable Logistic regression models were used to analyze the independent risk factors of vascular invasion in mass breast cancer, and to construct the nomogram prediction model. The efficacy of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve. Results Univariable Logistic regression analysis showed that the maximum diameter, Kaiser score, diffusion⁃weighted imaging signal, shape and related invasion signs were all associated with vascular invasion in mass breast cancer (all P<0.05). Further multivariable Logistic regression analysis showed that Kaiser score≥6 scores, hyperintensity on diffusion⁃weighted imaging, irregular shape, and presence of related invasion signs were independent risk factors for vascular invasion in mass breast cancer (all P<0.05). The area under the ROC curve (AUC) of the nomogram prediction model for mass breast cancer constructed by Kaiser score combined with diffusion⁃weighted imaging signals, shape and related invasion signs were 0.899 (95%CI: 0.859-0.939) and 0.827 (95%CI: 0.744-0.909) in the training set and validation set, respectively. The specificity and sensitivity were 0.845 and 0.840, respectively, in the training set, and 0.787 and 0.750, respectively, in the validation set. The calibration curve and Hosmer⁃Lemeshow test showed that the nomogram model had good consistency.  Clinical decision curve results showed that the nomogram could predict the vascular invasion of mass breast cancer with higher benefit. Conclusions The nomogram model of MRI image features based on Kaiser score constructed in this study is helpful for preoperative prediction of vascular invasion of mass breast cancer, and the model has high predictive efficiency, which provides a reference for the clinical preoperative prediction of vascular invasion of mass breast cancer.

Key words: Breast cancer, Vascular invasion, Magnetic resonance imaging, Kaiser score, Nomogram