Diagnostic value of multimodal Nomogram model combining 18F-FDG PET/CT and ultrasound for triple negative breast cancer
10.3760/cma.j.cn371439-20250414-00095
- VernacularTitle:18F-FDG PET/CT联合超声的多模态列线图模型对三阴性乳腺癌的诊断价值
- Author:
Qiaoliang CHEN
1
;
Xinyan QIN
;
Ruihe LAI
;
Shuangxiu TAN
Author Information
1. 南京大学医学院附属鼓楼医院 南京鼓楼医院核医学科，南京 210008
- Keywords:
Triple negative breast neoplasms;
Diagnosis;
Fluorodeoxyglucose F18;
Positron emission tomography computed tomography;
Ultrasonography, mammary
- From:
Journal of International Oncology
2025;52(9):560-565
- CountryChina
- Language:Chinese
-
Abstract:
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.
