Author:
Li Yajun,Han Guoqiang,Wu Xiaomei,Li Zhen Hui,Zhao Ke,Zhang Zhiping,Liu Zaiyi,Liang Changhong
Abstract
Abstract
To reduce the variability of radiomics features caused by computed tomography (CT) imaging protocols through using a generative adversarial network (GAN) method. In this study, we defined a set of images acquired with a certain imaging protocol as a domain, and a total of four domains (A, B, C, and T [target]) from three different scanners was included. In data set#1, 60 patients for each domain were collected. Data sets#2 and #3 included 40 slices of spleen for each of the domains. In data set#4, the slices of three colorectal cancer groups (n = 28, 38 and 32) were separately retrieved from three different scanners, and each group contained short-term and long-term survivors. Seventy-seven features were extracted for evaluation by comparing the feature distributions. First, we trained the GAN model on data set#1 to learn how to normalize images from domains A, B and C to T. Next, by comparing feature distributions between normalized images of the different domains, we identified the appropriate model and assessed it, in data set#2 and data set#3, respectively. Finally, to investigate whether our proposed method could facilitate multicenter radiomics analysis, we built the least absolute shrinkage and selection operator classifier to distinguish short-term from long-term survivors based on a certain group in data set#4, and validate it in another two groups, which formed a cross-validation between groups in data set#4. After normalization, the percentage of aligned features between domains A versus T, B versus T, and C versus T increased from 10.4 %, 18.2% and 50.1% to 93.5%, 89.6% and 77.9%, respectively. In the cross-validation results, the average improvement of the area under the receiver operating characteristic curve achieved 11% (3%–32%). Our proposed GAN-based normalization method could reduce the variability of radiomics features caused by different CT imaging protocols and facilitate multicenter radiomics analysis.
Funder
the National Natural Science Foundation of China
the National Key R&D Program of China
the Science and Technology Planning Project of Guangdong Province
Subject
Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Cited by
17 articles.
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