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Contrast Media & Molecular Imaging
Volume 2019, Article ID 4507694, 9 pages
https://doi.org/10.1155/2019/4507694
Research Article

Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma

1West China School of Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China
2State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
3School of Computer Science, Nanjing University of Science and Technology, No. 200, Xiaolinwei Road, Nanjing 210094, China
4Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China
5Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, China

Correspondence should be addressed to Rong Tian; moc.621@raelcunnaitgnor and Xuelei Ma; moc.liamg@ieleuxamrd

Received 9 October 2018; Accepted 5 December 2018; Published 25 February 2019

Academic Editor: Guillermina Ferro-Flores

Copyright © 2019 Xuejin Ou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose. To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in differentiating 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 co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different 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 (), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (); SUVstd, 2.04 versus 0.88 g/ml (); SUVmax, 10.69 versus 4.76 g/ml (); SUVpeak, 9.15 versus 2.78 g/ml (); TLG, 42.24 versus 9.90 (). 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 CT and PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the differentiation 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 differential diagnosis of breast lymphoma and carcinoma, even for the differentiation of subtypes of lymphoma.