Objective:To evaluate the diagnostic value of multimodal Nomogram model combining 18F-FDG PET/CT and ultrasound for triple negative breast cancer (TNBC) . Methods:A 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. Results:There 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. Conclusions:Nomogram model constructed by combining the SUV max of 18F-FDG PET/CT with the morphology and blood flow grading of ultrasound has a promising potential for diagnosing TNBC.

Objective:To investigate the value of conventional ultrasound combined with shear wave elastography (SWE) in the differential diagnosis of non-mass ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC) . Methods:A total of 102 patients with non-mass breast cancer admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from March 2019 to April 2022 were selected as the study objects, including 32 cases of DCIS and 70 cases of IBC. Conventional ultrasound parameters echo, microcalcification, location, posterior echo, blood flow, axillary lymph node, breast imaging reporting and data system (BI-RADS) score and SWE-related parameters maximum shear wave velocity (SWV max), minimum shear wave velocity (SWV min), mean shear wave velocity (SWV mean) and median shear wave velocity (SWV median) were compared between patients with non-mass DCIS and IBC. Binary logistic regression was used to analyze the independent factors for the differential diagnosis of non-mass DCIS and IBC. Based on the results of multivariate analysis, a nomogram prediction model was constructed and the predictive efficacy of the prediction model was evaluated by receiver operator characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used to evaluate the accuracy and practicability of the model. Results:There were statistically significant differences in blood flow ( χ2=8.47, P=0.004), axillary lymph nodes ( χ2=9.11, P=0.003), SWV max ( Z=-3.32, P<0.001), SWV mean ( t=3.00, P=0.003), SWV median ( Z=-2.69, P=0.007) between patients with non-mass DCIS and IBC. Multivariate analysis showed that, blood flow ( OR=3.56, 95% CI: 1.28-9.89, P=0.015), axillary lymph nodes ( OR=3.04, 95% CI: 1.10-8.42, P=0.032) and SWV max ( OR=1.40, 95% CI: 1.13-1.73, P=0.002) were independent factors for distinguishing non-mass DCIS from IBC. A nomogram prediction model was constructed based on blood flow, axillary lymph nodes and SWV max. ROC curve analysis showed that, the area under the curve of blood flow, axillary lymph nodes, SWV max, and prediction model for differential diagnosis of non-mass DCIS and IBC were 0.64 (95% CI: 0.52-0.76), 0.66 (95% CI: 0.55-0.77), 0.71 (95% CI: 0.60-0.81), and 0.79 (95% CI: 0.70-0.88), respectively, and the differential diagnostic value of prediction model was higher than that of blood flow ( Z=2.92, P=0.004), axillary lymph nodes ( Z=2.94, P=0.003), and SWV max ( Z=1.88, P=0.060) alone. The C-index of the prediction model for the differential diagnosis of non-mass DCIS and IBC was 0.77, and the calibration curve showed that the prediction probability of the prediction model was close to the actual probability. DCA showed that this prediction model could provide higher clinical net benefit and had certain clinical practicability. Conclusion:Blood flow and axillary lymph nodes in conventional ultrasound parameters and SWV max of SWE-related parameters are independent factors in the differential diagnosis of non-mass DCIS and IBC. The nomogram prediction model constructed by this method has a high value in the differential diagnosis of non-mass DCIS and IBC.

Objective To explore the difference in efficacy between multiparametric MRI (Mp-MRI) based on prostate imaging reporting and data system version 2 (PI-RADS v2) and abbreviated biparametric MRI (Bp-MRI) in detecting prostate cancer (PCa) and clinically significant prostate cancer (csPCa), and to evaluate the consistency of image interpretation between different readers. Methods The imaging, pathological and clinical data of patients with prostatic Mp-MRI in our hospital from February 2015 to June 2018 were retrospectively analyzed. At the beginning, 250 patients were randomly selected. Two radiologists visually evaluated the images of those patients using two 5-point scoring schemes based on Mp-MRI and Bp-MRI. The remaining cases were independently proceeded by one of the radiologists using two schemes respectively. Weighted Kappa test was used to assess the consistency of the results interpreted by the two radiologists. The receiver operating characteristic (ROC) curve was used to evaluate the efficiency of the two scoring schemes in detecting PCa and csPCa, and with Z test to investigate whether there was any difference in detection efficiency between the two schemes. Results Nine hundred and seventy eight patients were eventually enrolled in the study. The results of the consistency assessment showed that there was good agreement between the two radiologists, whether using Mp-MRI or Bp-MRI, with the weighted Kappa coefficient of 0.800 and 0.812, respectively. The ROC curve analysis showed that the area under the curve (AUC) of PCa detected by Mp-MRI and Bp-MRI was 0.873 and 0.879, respectively, and the AUC of csPCa detected was 0.922 and 0.932, respectively. In addition, there was no statistically significant difference between the AUC of PCa and csPCa detected by the two schemes (P>0.05). Conclusion The Bp-MRI scoring scheme has good stability in the evaluation of benign and malignant prostate, and its detection efficiency of PCa or csPCa is not lower than that of standard Mp-MRI based on PI-RADS v2.