ObjectiveTo evaluate the diagnostic value of multimodal Nomogram model combining¹⁸F-FDG PET/CT and ultrasound for triple negative breast cancer （TNBC）.MethodsA total of 61 breast cancer patients admitted at Nanjing Drum Tower Hospital， Affiliated Hospital of Nanjing University Medical School from November 2016 to May 2024 were selected as the study subjects， including 12 cases of TNBC and 49 cases of non-TNBC.¹⁸F-FDG PET/CT metabolic parameters maximum standardized uptake value （SUV_max）， mean standardized uptake value （SUV_mean）， minimum standardized uptake value （SUV_min）， tumor metabolic volume （MTV）， and total lesion glycolysis （TLG）， as well as the ultrasound parameters long diameter， short diameter， echogenicity， morphology， boundaries， posterior echogenicity， aspect ratio， microcalcifications， blood flow grading and Breast Imaging Reporting and Data System （BI-RADS） grading were compared between patients with and without TNBC. Least absolute shrinkage and selection operator （LASSO） regression was used for feature screening， and binary multivariate logistic regression analysis was conducted on the screened variables to obtain the independent influencing factors for diagnosing TNBC. The independent factors influencing the diagnosis of TNBC were established as Nomogram model and visualized. Receiver operator characteristic （ROC） curve， calibration curve and decision curve analysis （DCA） were used to evaluate the diagnostic efficacy， accuracy and clinical practicability of the model， respectively.ResultsThere were statistically significant differences in SUV_max（Z=-2.43，P=0.015）， SUV_mean（Z=-2.54，P=0.011）， morphology （P=0.004）， boundaries （χ²=4.86，P=0.028）， posterior echogenicity （P=0.027）， and blood flow grading （χ²=4.52，P=0.034） between TNBC and non-TNBC patients. LASSO regression screened out three variables： SUV_max， morphology and blood flow grading. Multivariate analysis showed that， SUV_max（OR=1.20， 95%CI： 1.04-1.38，P=0.012）， morphology （OR=0.02， 95%CI： 0.01-0.49，P=0.016）， and blood flow grading （OR=0.06， 95%CI： 0.01-0.74，P=0.028） were the independent influencing factors for diagnosing TNBC. A Nomogram model was established based on the above independent influencing factors. ROC curve showed that， area under the curve （AUC） of SUV_max， morphology， blood flow grading， and the Nomogram model were 0.73 （95%CI： 0.60-0.83）， 0.66 （95%CI： 0.52-0.77）， 0.67 （95%CI： 0.54-0.79）， 0.90 （95%CI： 0.79-0.96）， respectively， and the diagnostic value of the Nomogram model was higher than that of SUV_max（Z=2.71，P=0.007）， morphology （Z=3.61，P<0.001）， and blood flow grading （Z=2.51，P=0.012） alone. Calibration curve and DCA showed better accuracy and clinical practicability of the Nomogram model.ConclusionsNomogram model constructed by combining the SUV_maxof¹⁸F-FDG PET/CT with the morphology and blood flow grading of ultrasound has a promising potential for diagnosing TNBC.