Can Radiomics Analyses in 18 F-FDG PET/CT Images of Primary Breast Carcinoma Predict Hormone Receptor Status?

Molecular Imaging and Radionuclide Therapy

article

Mine Araz¹  Çiğdem Soydal¹  Pınar Gündüz¹  Ayça Kırmızı²  Batuhan Bakırarar³  Serpil Dizbay Sak²  Elgin Özkan¹ 

[1] Ankara University Faculty of Medicine, Department of Nuclear Medicine;Ankara University Faculty of Medicine, Department of Pathology;Ankara University Faculty of Medicine, Department of Biostatistics

关键词: Breast cancer;    PET/CT;    fluorodeoxyglucose;    hormone receptor;   

DOI  :  10.4274/mirt.galenos.2022.59140

来源: Turkiye Nukleer Tip Dernegi

【 摘 要 】

Objectives: This study aimed to investigate the role of preoperative 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features and metabolic parameters of primary breast tumors in predicting hormone receptor (HR) positivity. Methods: A total of 153 patients with breast carcinoma who underwent preoperative 18 F-FDG PET/CT were included. All PET/CT images were retrospectively reevaluated. Radiomics features of primary breast lesions reflecting tumor heterogeneity as well as standardized uptake value (SUV) metrics (SUV min , SUV mean , SUV max , and SUV peak ) and volumetric parameters such as metabolic tumor volume and total lesion glycolysis (TLG) were extracted by commercial texture analysis software package (LIFEx; https://www.lifexsoft.org/ index.php). WEKA and SPSS were used for statistical analysis. Binary logistic regression analysis was used to determine texture features predicting HR positivity. Accuracy, F-measure, precision, recall, and precision-recall curve area were used as data-mining performance criteria of texture features to predict HR positivity. Results: None of the radiomics parameters were significant in predicting HR status. Only SUV metrics and TLG were statistically important. Mean ± standard deviations for SUV mean , SUV max , and SUV peak for the HR-negative group were significantly higher than those in the HR-positive group (6.73±4.36 vs. 5.20±3.32, p=0.027; 11.55±7.42 vs. 8.63±5.23, p=0.006; and 8.37±6.81 vs. 5.72±4.86; p=0.012). Cut-off values of SUV mean , SUV max , and SUV peak for the prediction of HR positivity were 4.93, 8.35, and 6.02, respectively. Among data-mining methods, logistic regression showed the best performance with accuracy of 0.762. Conclusion: In addition to the relatively limited number of patients in this study, radiomics parameters cannot predict the HR status of primary breast cancer. SUV levels of the HR-negative group were significantly higher than those of the HR-positive group. To clarify the role of metabolic and radiomics parameters in predicting HR status in breast cancer, further studies involving a larger study population are needed.

【 授权许可】

Unknown