Title of article :
Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma
Author/Authors :
Ou, Xuejin West China School of Medicine - West China Hospital - Sichuan University - Guoxue Alley - Chengdu, China , Wang, Jian School of Computer Science - Nanjing University of Science and Technology - Xiaolinwei Road - Nanjing, China , Zhou, Ruofan West China School of Medicine - West China Hospital - Sichuan University - Guoxue Alley - Chengdu, China , Zhu, Sha West China School of Medicine - West China Hospital - Sichuan University - Guoxue Alley - Chengdu, China , Pang, Fuwen Department of Nuclear Medicine - West China Hospital - Sichuan University - Guoxue Alley - Chengdu, China , Zhou, Yi Department of Nuclear Medicine - West China Hospital - Sichuan University - Guoxue Alley - Chengdu, China , Tian, Rong Department of Nuclear Medicine - West China Hospital - Sichuan University - Guoxue Alley - Chengdu, China , Ma, Xuelei Department of Biotherapy - West China Hospital and State Key Laboratory of Biotherapy - Sichuan University - Chengdu, China
Pages :
9
From page :
1
To page :
9
Abstract :
To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in dierentiating between breast lymphoma and breast carcinoma. Methods. A total of 67 breast nodules from 44 patients who underwent 18F-FDG PET/CT pretreatment were retrospectively analyzed. Radiomic parameters and SUV metrics were extracted using the LIFEx package on PET and CT images. All texture parameters were divided into six groups: histogram (HISTO), SHAPE, gray-level cooccurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level dierent matrix (NGLDM), and gray-level zone-length matrix (GLZLM). Receiver operating characteristics (ROC) curves were generated to evaluate the discriminative ability of each parameter, and the optimal parameter in each group was selected to generate a new predictive variable by using binary logistic regression. PET predictive variable, CT predictive variable, the combination of PET and CT predictive variables, and SUVmax were compared in terms of areas under the curve (AUCs), sensitivity, specificity, and accuracy. Results. Except for SUVmin (p = 0.971), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (p ≤ 0.001), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (P < 0.001); SUVstd, 2.04 versus 0.88 g/ml (P = 0.001); SUVmax, 10.69 versus 4.76 g/ml (P = 0.001); SUVpeak, 9.15 versus 2.78 g/ml (P < 0.001); TLG, 42.24 versus 9.90 (P < 0.001). In the ROC curves analysis based on radiomic features and SUVmax, the AUC for SUVmax was 0.747, for CT texture parameters was 0.729, for PET texture parameters was 0.751, and for the combination of CTand PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the dierentiation between breast lymphoma and carcinoma. The combination of SUVmax and PET/CT texture analysis may be promising to provide an effectively discriminant modality for the dierential diagnosis of breast lymphoma and carcinoma, even for the dierentiation of subtypes of lymphoma.
Keywords :
18F-FDG , PET/CT , Lymphoma
Journal title :
Contrast Media and Molecular Imaging
Serial Year :
2019
Full Text URL :
Record number :
2618782
Link To Document :
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